Department of Computer Science

Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/16

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Author: search.filters.author.Dr. Muhammad Aksam IftikharSubject: search.filters.subject.FA22

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    Enhancing Brain Tumor Diagnosis through Deep Feature Learning and Spatial Context Awareness in MRI Scans
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Qasim Ali; FA22-RCS-022; Dr. Muhammad Aksam Iftikhar; LHR TP 9488
    Brain tumor classification is a critical task in medical diagnostics, where accurate and timely detection significantly influences treatment planning and patient outcomes. In this study, a hybrid deep learning framework which joins Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) is developed to classify brain tumors based on the BRATS 2021 dataset using the FLAIR modality. The CNN part learns local patterns (e.g. edges and textures) while the ViT part captures global dependencies over the whole images, which provides insights of the tumor regions in a holistic way. After performing pre-processing on the dataset, which comprised of 53,610 images, the images were partitioned into tumor and non-tumor groups. The training, validation, and test sets were created with 80%, 10%, and 10%, respectively. The listed model performed two independently trained models for 100 epochs at first, then is applied a feature fusion technique which exploits the complementarity of both architectures. The hybrid model was assessed based on conventional metrics like precision, recall, F1-score, and accuracy. The result confirmed that the hybrid method achieved better performance than the individual CNN and ViT models, with improvements in precision and strengthened classification capability. The study emphasizes how hybrid deep learning techniques can improve clinical applications by alleviating challenges related to data variability, noise, and image complexity. The proposed method combining local and global feature extraction methods ensures valuable typing providing a reliable and scalable implementation of automatic brain tumor classification helping improve the accuracy and efficiency of healthcare related problem solutions and medical diagnostics.
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    Self-Attention Based Multi-Model Fusion Mechanism for Diabetic Retinopathy Grading
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Sana Naseem; FA22-RCS-023; Dr. Muhammad Aksam Iftikhar; LHR TP 9332
    Among the five basic human senses, vision is the most versatile, which depends primarily on the flawless working of human eyes. An eye condition called diabetic retinopathy (DR) is caused by diabetes. Retinal lesions are important signs of DR, and they can be used for early diagnosis. This research will identify and evaluate any pathological alterations brought on by the development of DR. In this research, an attention based deep learning method is proposed to classify Diabetic Retinopathy (DR), wherein CLAHE preprocessing is first used to enhance the image contrast. The publicly accessible dataset APTOS 2019 is utilized in the experiments to evaluate the effectiveness of proposed methodology. However, this dataset has imbalanced class distribution, which was balanced using different data augmentation strategies. For effective feature learning, four pre-trained models namely Inception v3, VGG-19, ResNet-50, and DensNet-121 are modified by adding self- attention blocks following each convolutional base of the pre-trained models. These self-attention blocks enable the model to selectively focus on important parts of DR lesions. Subsequently, the extracted features are fused using late fusion, and a Multi- Layer Perceptron (MLP) classifier is employed to categorize DR stages. The proposed method was validated by performing ablation studies for each pre-trained model. Specifically, classification evaluation metrics including precision, recall, F1- score, and accuracy were computed for each pre-trained model with and without the attention block. The comparison of results clearly showed the effectiveness of adding the attention blocks to each model. Further, the final MLP classifier trained on the fused convolutional features from all pre-trained models was also evaluated for DR grading, which resulted into precision, recall, F1-score, and accuracy of 0.83%, 0.81%, 0.82% and 0.90%, respectively. These results were compared with state-of-the-art methods to show that the proposed method outperforms existing methods significantly. Therefore, the proposed method can be a useful tool for providing a secondary opinion to medical practitioners for DR grading and classification.