Browsing by Author "Dr. Usama Ijaz Bajwa"

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A Robust Attention-based 3D CNN Model for Fire and Smoke Detection from Videos
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Rimsha Shoukat; FA21-RCS-014; LHR TP 8677; Dr. Usama Ijaz Bajwa
The unforeseen occurrence of a fire eruption has significant and adverse impacts on all aspects of human existence, including human life, land, industry, forests, and animal populations. The emergence of smoke before the apparent signs of fire makes smoke detection very advantageous in terms of promptly identifying fires. This early detection capability holds significant potential for preserving numerous lives and mitigating the occurrence of catastrophic events and associated losses. The problem of detecting smoke and fire using video-based methods presents significant challenges due to the considerable variability in their color, brightness, and shape. Most prior researchers conducted individual studies focused on the categorization of fire or smoke. Furthermore, video classification studies focused on a 2D approach, which was inadequate due to its inability to capture temporal information, so it was not able to learn the difference between frames. The convolutional neural network (CNN) has demonstrated exceptional performance in several domains, hence establishing itself as the leading approach. This study presents a robust classifier based on a three-dimensional convolutional neural network (3D-CNN) specifically developed to classify events into three distinct classes: fire, smoke, and neutral. The classifier is meant to effectively process video data. 3D models have the capability to acquire spatial and temporal characteristics from video sequences, rendering them appropriate for undertaking such tasks. The proposed 3D-CNN model had a notable test accuracy of 90% and showcased outstanding performance with an accuracy of 96% when subject to cross data [1] that have seven smoke videos. To enhance its capabilities, the model integrates the CBAM (Convolutional Block Attention Module) attention mechanism. The utilization of this approach allows the model to concentrate on significant regions of interest (ROI) throughout the entirety of the frame, hence enhancing its precision in categorizing complex attributes inside video frames. By integrating the attention mechanism, the model attained a remarkable gain of 6% in test accuracy with an accuracy rate of 96% and an F1-score of 98%.
A Robust Multi-Camera Deep Person Re Identification Framework Using Spatiotemporal Context Modelling
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Fatima Zulfiqar; SP20-RCS-005; LHR TP 7601; Dr. Usama Ijaz Bajwa
In this thesis, research work is based on a robust automatic re-identification of a person from multiple non-overlapping cameras under variable and dynamic environmental conditions for an accurate re-identification and retrieval of targeted person identities. Person Re-Identification (ReID) aims at identifying query person of interest (POI) assigned with a unique identity label across multiple non-overlapping cameras. The query POI can be either an image or a video sequence. Person ReID has gained quite increasing attention among various research and developer communities in recent years. Several research challenges including occlusion, variable viewpoint, misalignment, unrestrained poses, background clutter, etc. are the major challenges in developing robust, lightweight, end-to-end trainable person ReID models. To address these issues, an attention mechanism that comprises local part/region aggregated feature representation learning is presented in this research by incorporating long-range local and global context modeling. The part-aware local attention blocks are aggregated into the widely used modified pre-trained ResNet50 CNN architecture as a backbone employing two attention blocks i.e. Spatio-Temporal Attention Module (STAM) and Channel Attention Module (CAM) thus improving both local and global feature representation learning. The spatial Attention block of STAM can learn contextual dependencies between different human body parts regions like head, upper body, lower body, and shoes from a single frame. On the other hand, the temporal attention modality is capable to learn temporal contextual dependencies of the same person’s body parts across all video frames. Lastly, the channel-based attention modality i.e. CAM can model semantic connections between the channels of feature maps. These STAM and CAM blocks are combined sequentially from a unified attention network named Spatio-Temporal Channel Attention Network (STCANet) that will be able to learn both short-range and long-range global feature maps respectively. Extensive experiments are carried out to study the effectiveness of STCANet on three images and two video-based benchmark datasets i.e. Market- x 1501, DukeMTMC-ReID, MSMT17, DukeMTC-VideoReID, and MARS. K reciprocal re-ranking of gallery set is also applied in which the proposed network showed significant improvement over these datasets in comparison to the state-of-the-art by achieving (mAP/Rank-1) score of (95.5/94.5), (90.7/92.3) and, (74.4/84.5) on Market-1501, DukeMTMC-ReID, and MSMT17 dataset respectively. In addition, the proposed modified STCANet also showed significant performance improvement in comparison to state-of-the-art methods by achieving (mAP/Rank-1) score of (96.6/97.1), (85.3.7/89.1) on DukeMTMC-VideoReID and MARS dataset respectively Lastly, to study the generalizability of STCANet on unseen test instances, cross-validation on external cohorts is also applied that showed the robustness of the proposed model. The proposed STCANet is lightweight, end-to-end trainable, and can be easily deployed to the real world for practical applications
Automated Detection and Classification of Brain Tumor from MRI Images using Machine Learning Methods
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2018) Ghulam Gilanie; FA14-PCS-007; LHR TP 5805; Dr. Usama Ijaz Bajwa
The focus of this thesis is to report an automated, efficient, and robust method of brain tumor detection and classification from Magnetic Resonance Images (MRI) images. Clinically, it is a challenging issue faced by the researchers working in this domain. In routine health care units, Magnetic Resonance (MR) scanners are being used to generate a massive number of brain slices, underlying the anatomical details. Pathological assessment from this medical data is being carried out manually by the radiologists or neuro oncologists. Due to huge volume of brain anatomical data produced by MRI scanners, it is almost impossible to manually analyze every slice. Conclusively, if automated protocols are executed for auto-interpretation; not only the radiologist will be assisted but also a better pathological assessment process would be expected. Several methods have been suggested to address this problem, but still, accuracy, robustness and optimization is still an open issue to address. The development of such automated procedures is difficult due to complex organization of brain cells, several types of tumor, difference in medical traits of a specific ethnicity and many more factors. To achieve the target, research has been started from reviewing the most popular and prominent state-of-the-art methods. Based upon the reviewed literature, automated brain tumor detection and classification techniques have been reported with high computational cost, low classification rates, detection and classification of only one or a few of brain tumor types, lack of robustness, etc. Therefore, step wise research and experiments based upon empirical scientific methodology have been performed in order to achieve the objectives of brain tumor classification. In the first step, a research activity has been performed to report a colorization method with the aims to enhance the visualization, cell characterization and interpretation of brain cells. The high dimensional brain data scanned through MRI embodied in gray scale, if converted, represented, mapped and/or visualized in colored versions, irrefutably, more definitive and more accurate the pathological assessment process will be. Several methods have been reported to represent brain MRI data in color with high computational xii complexity. In this research activity, an efficient method of colorization using frequencies from visible range of color spectrum, has been proposed to embody the variations and sensitivity of the brain MRI images. The experiments have been performed on a locally developed dataset. Side by side visual comparison based on multiple MRI sequences of identical subjects by domain experts have proved the adequate success and fruitfulness of the story. The reported method of colorization as a protocol has also been deployed in Department of Radiology and Diagnostic Images, Bahawal Victoria, Hospital, Bahawalpur (BVHB), Pakistan. Radiologists are using this tool for visual interpretation and monitoring of the patients for their assessment and clinical decision making. In second step, an automated approach using Gabor filter and Support Vector Machines (SVMs), for the classification of brain MRI slices as normal or abnormal has been reported. Accuracy, sensitivity, specificity and AUC-value have been used as standard quantitative measures to evaluate the proposed algorithm. To the best of our knowledge, this is the first study in which experiments have been performed on The Whole Brain Atlas - Harvard Medical School (HMS) dataset, achieving an accuracy of 97.5%, sensitivity of 99%, specificity of 92% and AUC-value as 0.99. To test the robustness against medical traits based on ethnicity and to achieve optimization, a locally developed dataset has also been used for experiments and remarkable results with accuracy (96.5%), sensitivity (98%), specificity (92%) and AUC-value (0.97) were achieved. Comparison with state-of-the art methods proved the overall efficacy of the proposed method. In third step of the thesis, a method has been proposed to classify brain MRI image into brain related disease groups and further tumor types. The proposed method employed Gabor texture followed by a set of more distinguished statistical features. These features are then used by SVM to classify the brain disorder. K-fold strategy has been adapted for cross validation of the results to enhance generalization of SVM. Experiments have been performed to classify brain MRI images as normal or belonging to either of the common diseases, such as cerebrovascular, degenerative, inflammatory, and neoplastic. Neoplastic disease is further classified into glioma, meningioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, or sarcoma. Standard quantitative evaluation measures, i.e., accuracy, specificity, sensitivity, and AUC-value have been used to test xiii performance of the developed system. The proposed system has been trained on complete dataset of HMS, so the trained model has the ability to deal with a wide range of brain abnormalities. Further, to achieve robustness, a locally developed dataset has also been used for experiments. Remarkable results on different orientations, sequences of both of these datasets as per accuracy (up-to 99.6%), sensitivity (up-to 100%), specificity (up-to 100%), precision (up-to 100%) and AUC-value (up-to 1.0) have been achieved. The proposed method classifies the brain MRI slices into defined abnormality groups. It can also classify the abnormal slices into tumorous or non-tumorous one. The major achievement of the developed system is its auto classification of tumorous slices into the slices having primary tumor or secondary tumor and their further types, which possibly could not be determined without biopsy. In fourth step of the thesis, results achieved through the proposed method of brain tumor classification have been validated on cross data set. The drive of this research activity is to verify the robustness of the reported approach. For this, the model has been trained completely on one data set, while tested completely on another one. A benchmarked dataset HMS and a locally developed dataset BVHB dataset has been used for this purpose. To ensure its robustness, complete HMS dataset was used to train the model and BVHB was used to test the trained model and vice versa. Standard evaluation measures, i.e., accuracy, specificity, sensitivity, precision and AUC-value have been used to evaluate the system. It has been established that the proposed method deals with multiformity and variability of brain MRI data. Overall, suppositions regarding robustness of the proposed method were attained with maximum measures as per accuracy as 92%, specificity as 92%, sensitivity as 93%, precision as 92%, and AUC-value as 0.93. The overall results achieved through the proposed method, manifests that it is robust, efficient and reliable. It has been trained on a large volume of multi-orientations, multi-sequences belonging to multi-datasets to deal with multiformity and to face variabilit
Automated Detection of Early Pulmonary Nodule in Computed Tomography Images
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2019) Ahmad Usama Tariq; FA16-RCS-012; LHR TP 5777; Dr. Usama Ijaz Bajwa
Classification of lung cancer in CT scans majorly have two steps, detect all suspicious lesions also known as pulmonary nodules and calculate the malignancy. Currently, a lot of studies are about nodules detection, but some are about the evaluation of nodule malignancy. Since the presence of nodule does not unquestionably define the presence lung cancer and the morphology of nodule has a complex association with malignant growth, the diagnosis of lung cancer requests cautious examinations on each suspicious nodule and integrateed information every nodule. We propose a 3D CNN CAD system to solve this problem. The system consists of two modules a 3D CNN for nodule detec tion, which outputs all suspicious nodules for a subject and second module train on XGBoost classifier with selective data to acquire the probability of lung malignancy for the subject
Classification of MGMT Promoter Methylation Status in Brain Tumor MR Images using Deep Learning
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Sohaib Iqbal; FA21-RCS-005; LHR TP 8470; Dr. Usama Ijaz Bajwa
Glioblastoma is the most dangerous brain tumor type (worst prognosis), having the least survival rate of less than a year. O6-methylguanine-DNA methyltransferase (MGMT) promoter status “methylation” adds a favorable prognosis and is a reliable indication of cancer's chemosensitivity. Therefore, determining the MGMT status of a brain tumor patient is essential for treatment planning. Surgical tissue sample removal is required to conduct genetic testing for tumors. The genetic characterization of the tumors may then take a few weeks to establish after that. Non-invasive diagnosis using MR images plays a significant role in the early detection of the deadly disease by using Magnetic Resonance Imaging (MRI) to detect the tumorous regions using deep learning techniques. The main requirement for detecting MGMT status is obtaining the sub-regions of brain tumor from MRIs. Previous studies have worked on Brain tumor segmentation and MGMT classification separately, few of the studies have worked on segmentation and classification together but have used selective modalities for MGMT Prediction. Since each modality has its own significance and MGMT promoter status lies in the tumorous region, this study proposed a pipeline of segmentation and classification models using stacked multimodalities of MRI scans of the benchmark dataset BraTS2021. The pipeline is divided into two phases. The first phase uses a stack of multiple MRI modalities to segment the brain tumor into sub-region using a 3D Residual U-Net Architecture, and the second phase uses the segmentation model's output (stacked multimodalities tumor voxel) to determine the MGMT promoter status using a 3D ResNet 10 Classifier. With the help of pipeline, classification model received precise information about the tumorous regions which helped in efficient prediction of the MGMT status present in the brain tumor. The proposed pipeline has therapeutic value because it can assist radiologists in diagnosing brain tumors more efficiently and precisely by reducing the subjectivity and variability of human interpretation. The segmentation and classification pipeline for brain tumors x can potentially increase the consistency and objectivity of diagnoses. Furthermore, it can also assist radiologists in predicting the MGMT status without using surgical equipment and can help in treatment decision making. The segmentation phase of the proposed pipeline yielded promising results with average dice scores of 0.81, 0.84, and 0.80 for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) on validation. The classification phase achieved ROC-AUC score of 0.66 on validation.
Deception Detection using Facial Action Coding System in Videos
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Hammad-ud-din Ahmed; SP17-RCS-015; LHR TP 5974; Dr. Usama Ijaz Bajwa
Humans lie to each other on a regular basis in one form or another. To lie to someone is to share altered facts. Facts are important for decision making in every situation. Without unaltered facts, potentially harmful decisions can be made which can alter someone’s life in ways that may not want. This is why it is important to catch misinformation before any harm can be caused by them. Deception detection in videos has gained traction in recent times for its various real-life applications. When a person lies, they show facial expressions, also known as microexpressions so fast that they can be ignored by the untrained eye. Microexpressions can be used as a basis for creating a deception detection system. Facial Action Coding System is utilized as a way to encode and extract data from facial muscle movement during truthful and deceptive confessions. This data is used to train a deep learning model that utilizes long short-term memory (LSTM) to train and create the system. The real-life trial dataset is used to train and test the system provided one of the best facial only approaches to deception detection. Cross-data validation is also tested using the Real-life trial dataset, the Silesian Deception Database, and the Bag-of-lies Deception Database. Cross-data validation is something that has not yet been attempted by anyone else for a deception detection system which provides unique insight for the field. The results show that adding different datasets to train a neural network for the sake of creating a deception detection system worsens the accuracy of the system but the reasoning behind the poorer results actually encourages attempts at creating better datasets
Deep Learning-Based Prediction of SARS-CoV 2 (COVID-19) and its Severity Classification using Multimodal Chest Radiography Images
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Mughees Ahmad; SP20-RCS-019; LHR TP 7595; Dr. Usama Ijaz Bajwa
The new COVID-19 emerged in a town in China named Wuhan in December 2019 and since then this deadly virus transmitted all over the world. This transmittable disease has infected 262 million people worldwide and 5.2 million deaths till November 2021. As we can see the rapidly spreading of this pandemic, different countries are facing limited resources such as medical test kits and ventilators because of the number of positive cases that have been increased out of control. In this devastating situation, it is very compulsory to develop an easily available, low-priced, and automatic deep learning model for COVID-19 prediction through chest radiography images (CRIs) such as X-rays, and CTs. The proposed study is using chest radiography images to detect chest infections such as bacterial, viral, and COVID-19 infection as X-rays and CTs give significant information about this deadly infection. The recent hybrid deep learning (DL) techniques can be used along with chest radiography images (CRIs) for the faster and accurate detection of different chest infections including COVID-19 and its severity levels (i.e., negative for pneumonia, atypical appearance, indeterminate appearance, and typical appearance). Therefore, a novel hybrid model named Lightweight Residual_Bi-GRU uses residual blocks and Bi-directional gated recurrent unit (Bi-GRU) for automatic and correct detection of non-COVID and COVID-19 infections by using preprocessed chest radiography images (CRIs). Lightweight Residual_Bi-GRU is used for the recognition of two-class classification (normal and COVID-19), three-class classification (normal vs COVID-19 vs viral pneumonia), four class classification (normal vs COVID-19 vs viral pneumonia vs bacterial pneumonia), and different COVID-19 severity types' classification. The presented model provides a classification accuracy of 99.5%, 98.4%, 90.2%, and 80.7% for 2 class, 3 class, 4 class, and COVID-19 severity levels classifications. The results prove that radiologists and medical officers can adopt this method for the screening of chest infections where test kits are limited.
Detection of Fire and Smoke from Video Sequences
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Ali Hussain; SP18-RCS-003; LHR TP 7280; Dr. Usama Ijaz Bajwa
Since the introduction of deep neural networks in object detection, fire and smoke has been in the focus of many researchers. The recent state of the art Convolution Neural Network (CNN) based architectures provide more than 95% fire-smoke detection accuracy but in controlled environments, e.g., a fire in server rooms or production lines. Fire burns differently in certain environments; wildfires and domestic fire have distinctive characteristics and burning patterns. A fire detection system should be strong enough to better generalize different fire burning patterns. Training such a system needs a massive amount of annotated data describing unique fire patterns. In this research, a deep neural network-based fire and smoke detection system will be proposed. This network's primary focus will be consuming less training data for better generalization to achieve high accuracy with a low false-positive rate. The results have been reported using the ROC, accuracy, false positive rates
Enhancing Crime Classification in Surveillance Videos for Real Time Monitoring
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Salman Ghauri; SP19-RCS-027; LHR TP 7590; Dr. Usama Ijaz Bajwa
Anomalies can be detected with the help of patterns and events that differ from the normal flow of events. The paradigms of surveillance may relate to abuse, fights, road accidents and snatchings, etc. In real-world surveillance, finding unusual events in these massive video streams is a difficult endeavor, since they often occur inconsistently. However, deep learning-based anomaly detection helps reduce human labor and its decision-making ability can be compared to that of humans, thus ensuring the safety of the public. In the majority of reported studies, anomalies are detected from surveillance videos based on binary classification. The reported approaches did not cover other anomalous events from surveillance videos, including abuse, fights, vehicle accidents, shootings, stealing, vandalism, and robberies. This paper proposes an intelligent anomaly detection framework based on deep features that can operate more efficiently in surveillance networks. In the proposed framework, spatial-temporal features are first extracted from a series of frames by passing them through a CNN model that has been pretrained. Analyzing the frames in a sequence can be beneficial in detecting anomalous events. Once the deep features have been extracted, the data is then passed to the Long Short-Term Memory (LSTM) model. The model can accurately classify ongoing anomalies/normal events in complex surveillance scenes of smart cities. A dataset from the University of Central Florida (UCF) Crime video dataset is used to perform extensive experiments on anomaly detection. We report an increase in data accuracy of 47.83% over state-of-the-art methods for UCF-Crime datasets.
GAME FOR DYSLEXICS
(Library Information Services, COMSATS University Islamabad, Lahore Campus., 2020-11-20) Noor E Eman; FA16-BCS-286; Dr. Usama Ijaz Bajwa; LHR TP 6120
The present project will be an empirical endeavour for the development of a reliable and valid diagnostic and rehabilitation tool for the children with reading disorder. It is seen that the activity of diagnosis and rehabilitation is boring for the children, consequently being less effective for them. Therefore, to devise a technique through which children learn more is the focus of this project. Children now a days show keen interest in computer games. Due to the proposed tool, which is a series of games, children will be able to differentiate between different sounds, words and letters. This would be done by different techniques and creating different scenarios and levels. This report presents our serious games created to screen and train different abilities which are usually impaired in children with dyslexia. We primarily test the cognitive abilities which are deeply related to the children’s future reading skills, i.e., visual search ability, rapid identification of visual inputs, visual sequential and auditory memory and the capability to associate visual and auditory stimuli. To this aim, we discuss here a series of serious games designed to train specific skills that have been proven to be effective against dyslexia according to Orton Gallium Study. This game would be a good platform for rehabilitation along with providing the results of performance at the end of game. The game would be developed using unity 3D engine, which provides variety of tools for game creation.
Human Action Recognition in Low-Resolution Videos Based on Spatio-Temporal Features
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Samia Akram; FA21-RCS-015; LHR TP 8678; Dr. Usama Ijaz Bajwa
In recent years, the field of computer vision and pattern recognition has devoted a lot of research focus to the study of human action. Most of the current action recognition research focuses on high-quality videos with clearly apparent actions. Most actions are of limited quality and take place at a distance, making it difficult to identify them. Therefore, the issue of low video quality is still under-researched and difficult to solve in practical implementation. The goal of this study is to establish a deep learning based framework for recognizing human actions in low-quality video by utilizing spatial-temporal features. This research focuses on the use of annotated dataset TinyVIRAT-v2 of action recognition videos recorded in low quality in order to examine the applicability of deep learning architecture for action recognition from videos of low perceptual quality. First, in the proposed framework datasets will be pre-processed and spatial-temporal features will be retrieved using a CNN-based feature extractor. The extracted features will then be fed into a 3D custom model for classification, which is based on resnet50 as backbone network and c3d with a SoftMax layer for multiclass prediction, allowing for its implementation as a real-world practical application. Upon classifying, every testing video is allocated a predicted category, and the cumulative outcomes are evaluated using performance measure F1-Score which is 0.68.
Multiclass Weeds and Crops Classification and Segmentation using Deep Learning
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Seemab Ayub; FA21-RCS-009; LHR TP 8675; Dr. Usama Ijaz Bajwa
The world's population has increased exponentially with the increase in food demand, so it's essential to focus on factors that affect the quality and quantity of food. Some basic natural factors are climate, soil, pests, and weeds. Weeds are unwanted plants in yields that compete with the crops and consume a lot of nutrients that affect the quality and growth of crops. The separation of these toxic weeds is a challenging task because of their color and quantity. Pakistan is an agricultural country, and almost a big part of its economy depends on agriculture. The growth of this department does not match the population growth; the reason behind this is the presence of weeds. Automated technological interventions represent the optimal approach for weed removal. However, notable challenges persist within the technological landscape, including the absence of comprehensive datasets and the need for well-automated systems specifically tailored for crop weed classification and segmentation. Focusing on these issues, most of the researchers are working on different techniques, but the inadequate size of the dataset is still a problem for researchers, so this study performed to generate an efficient large dataset by merging different small datasets on the base to pick only those classes of weed and crops that belong to Pakistan; also an effective system using UNet variations (UNet, Attention UNet, and Attention Residual UNet(ARUNet)) and ResNet where ARUNet has performed very well for segmentation process and especially the Attention mechanism boosted the segmentation process, on the other hand ResNet has performed best for classification process on our dataset (24- classes and 120000 images). The accuracy of 99% achieved for classification process and 95% for segmentation.
Panoptes
(Library Information Services, CUI Lahore, 2022) M. Abid Ullah; SP18-BCS-120; Dr. Usama Ijaz Bajwa
Nowadays we can find security cameras everywhere, from roads to malls, we are under surveillance. A lot of people install security cameras in their houses and the sole reason for installing security cameras is safety. Everyone is worried about the safety of someone, some people are worried about themselves, some about their family, and some are concerned about the safety of their friends. In short, everyone cares about being safe. But how about increasing the security up a notch? How about people installing software with their security cameras that will automatically detect suspicious activities and report authorities? The primary goal of this project is to develop a system that is capable to detect any anomalous and suspicious activities from given video frames captured by surveillance cameras and reporting them to authorized personnel for immediate actions. It is very common in surveillance that anomalies go unnoticed by the guards on the spot. Thousands of cameras are installed on streets and roads that record everything, but that recording is only used later because no one was watching a live video stream at that exact moment. But what if the software is always watching these thousands of live video streams? This means that every anomalous activity or crime that occurs and is recorded by these cameras will be reported instantly. This software will use an advanced Machine learning model to recognize suspicious activities and report them to proper authorities.
Sentinel: Smart Surveillance System with Automated Anomaly Detection
(Library Information Services, CUI Lahore, 2022) Hammad Ali; FA20-BCS-087; Dr. Usama Ijaz Bajwa
This project addresses the critical need for efficient and real-time anomaly detection in surveillance systems, considering the widespread deployment of surveillance cameras in diverse environments. Traditional server-based approaches pose challenges related to cost, network strain, and responsiveness. Leveraging edge computing and deep learning, our project aims to develop a cost- effective solution. Success criteria involve achieving state-of-the-art accuracy, real-time inference and streaming, and seamless integration with existing networks. Our goal is to explore existing solutions and propose an architecture that optimizes model performance on edge devices while
TalkSee
(Library Information Services, CUI Lahore, 2021) Zenia Kiran; LHR TP 7504; FA17-BCS-129; Dr. Usama Ijaz Bajwa
Communication has a crucial role in our daily whether at work, at home, abroad, in education or to enhance our social as well as business network. To have an effective communication, a language plays a vital part that helps to build connection among people from different cultures. there are still some gaps while interacting with people from different cultures and these are due to language barriers. In the increasingly integrated global business community, due to an increasing need to communicate with the people all around the world, the language barrier between different cultures needs to be removed. One solution is to make people learn multiple language, that is worst one, because it is impossible mankind to learn all the native tongue of world. Therefore, by keeping in mind, the problem of language barrier we proposed a solution named TalkSee. TalkSee is a web-based chat application that will allow people to communicate with each other on one platform by translating their text messages, written in their chosen language, to some other language on the receiver’s end in the form of their talking Avatar to provide them with the best possible experience of communication
Transfer Learning based Deep Learning Approach for Brain Tumor Segmentation from MR Images
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Rehan Raza; SP20-RCS-018; LHR TP 7594; Dr. Usama Ijaz Bajwa
Glioma is the most prevalent and dangerous type of brain tumor which can be life-threatening when its grade is high. The early detection of these tumors can improve and save the life of the patients. The automatic segmentation of brain tumors from magnetic resonance imaging (MRI) plays a vital role in treatment planning and timely diagnosis. Automatic segmentation is a challenging task due to the massive amount of information provided by MRI and the variation in the location, type, and size of the tumor. Therefore, a reliable and authentic method to segment the tumorous region from healthy tissues accurately is an open challenge in the field of deep learning-based medical image analysis. This thesis presents an end-to-end framework for automatic 3D Brain Tumor Segmentation (BTS). The proposed model is a hybrid of the deep residual network and U-Net model (dResU-Net). The residual network is used as an encoder in the proposed architecture with the decoder of the U-Net model to handle the issue of vanishing gradient. The proposed model is designed to take benefit from low and high features. In addition, shortcut connections are employed in residual convolutional blocks and skip connections between residual and convolutional blocks are utilized in the proposed architecture to accelerate the training process. The proposed architecture achieved promising results with the average dice score of 0.8357, 0.8660, and 0.8004 for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET), respectively, on BraTS 2020 dataset. Furthermore, the proposed dResU-Net model is also compared with modified 3D U-Net and pre-trained ResU-Net34 and ResU Net50 to check the performance of the proposed model over the pre-trained models. To demonstrate the robustness of the proposed model in real-world clinical settings, validation of the trained model on an external cohort is performed on randomly selected 50 patients of the BraTS 2021 benchmark dataset. The achieved dice scores on external cohorts are 0.8400, 0.8601, and 0.8221 for TC, WT, and ET, respectively. The comparison of results of the proposed technique with the state-of-the-art techniques indicates that dResU- x Net can significantly improve the segmentation performance of brain tumor sub-regions.