Department of Computer Science

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Author: search.filters.author.Dr. Muhammad Aksam IftikharSubject: search.filters.subject.fa19

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    Segmentation of Brain Tumor using Deep Learning Techniques
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2022) Muhammad Hashir Khan; FA19-RCS-026; Dr. Muhammad Aksam Iftikhar; LHR TP 8357
    Cancer is one of the fatal types of disease in the World. A brain tumor is a type of cancer that appears in the glial cells of the brain. There exist two types of brain tumors. One is High-grade glioma and the other is low-grade glioma. The survival rate for HGG is very low while according to history most patients diagnose with LGG survive this disease. To detect the tumor in the brain one of the popular techniques is the Magnetic Imaging Resonance (MRI). It has four different modalities and neurologists after observing the different MRI modalities diagnose the tumor location and category. But manually detecting the tumor from MRI scans is a difficult task. There is always a need to segment the tumor region automatically from an MRI scan. To solve this problem, many researchers proposed different solutions. Many researchers use deep learning models to address this issue. One of the earliest networks used for this was Convolutional Neural Network (CNN) and it gives good results for tumor segmentation tasks. In this research, the approach we used is based on the ensemble method. In this approach, we use three different U-Net models to train them from scratch and predict the results on each model separately. After that, we ensemble all three models predicted results applying the majority voting technique and produce a result on testing data. Our proposed ensemble method produces a dice score of 0.86, 0.88, 0.89, and Hausdorff distance 2.0, 2.0., 2.0 for the three categories of a tumor, Enhancing tumor, Whole tumor, and Tumor core respectively on the testing dataset. Our results are better than many comparable state of the art method including the Brats 2019 challenge prominent papers.
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    Survival Prediction of Glioma Patients Based on Radiomic Features
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Talha Naeem; FA19-RCS-029; LHR TP 8464; Dr. Muhammad Aksam Iftikhar
    Our brain is the most important part of our body that controls and commands all the functions. It Takes information from different organs of the body and five main senses and then commands the tissues to perform specific tasks. Brain administrates the motions, creativity, emotions, and memory. The brain is enclosed within the skull, and it consists of Cerebrum, Cerebellum and Brainstem. Cerebrum hemisphere portion is further split into lobes. Each hemisphere includes the Temporal Lobe, Frontal Lobe, Parietal Lobe, and Occipital Lobe. All these hemispheres are once further divided into their areas to accomplish the specific factions. Each lobe cannot accomplish any of the body function without the help of other lobes separately. The relationship between the lobes of the brain and left and right hemisphere is very complex. The left hemisphere deals and commands to the right portion of body and right hemisphere deals and commands to the left portion of body. Gliomas are unique and very extensively recognized categories of the brain tumor. Glioma is a brain tumor category that starts producing in the brain or the spine glial cabals. It is a kind of sturdy cell in our brain. For precise and strong diagnosis of glioma patients both the segmentation into sub-regions and overall survival (OS) prediction tasks are very important. These tasks are used for the better treatment arrangement and risk influence investigation. Form the past couple of year’s investigation in brain tumor has seen remarkable progress. Many researchers present admirable work for better treatment of glioma patients, better treatment arrangement and OS prediction time. However, still to the current era, huge gap exists for precise segmentation and OS prediction task of the glioma patients. Researchers are trying to progress in the complexities and challenges of brain tumor. In this proposed methodology, our set of rules achieve good performance in OS prediction task of glioma patients by using machine learning outline. We perform segmentation of brain sub-regions on magnetic resonance imaging (MRI) scans. Based on segmented sub- regions, we extract the radiomic features such as intensity, shape, and texture. The proposed approach employed on multimodal brain x tumor segmentation (BraTS) with OS prediction task for BraTS-2020 dataset and trained on the random forest (RF) model for the OS task for the prediction of survival days for brain tumor patients. The evaluation of the performance measures is carried out for segmentation process by a pre-processing technique and OS prediction by measuring accuracy of the RF model and we obtained the results of 59% and 51% for training and testing of glioma patients respectively.