M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/36
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item 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 BajwaGlioblastoma 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.Item 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 BajwaAnomalies 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.Item 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 BajwaGlioma 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.Item 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 BajwaHumans 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 datasetsItem 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 BajwaClassification 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