M.Phil / MS

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Detecting Breast Cancer from Histopathology Images Using CNN-Based Framework
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Tahreem Zara; CIIT/SP23-RCS-028/LHR; Dr. Zulfiqar Habib; LHR TP 9706
Breast cancer remains one of the most frequently diagnosed cancers among women globally, with histopathology analysis serving as the gold standard for diagnosis. But manual examination of histopathology slides takes a long time, is prone to making mistakes, and often has differences between observers, especially in complicated cases. To overcome these constraints, this study introduces a lightweight and deployable deep learning framework designed to automate breast cancer detection utilizing histopathology images from the BRACS dataset. The proposed framework utilizes the DenseNet169 model pre-trained on ImageNet; we fine-tuned the last 30 layers after testing different depths. We resized the input images to 1024×1024 to preserve important morphological details, and we employed simple augmentations, such as rotation and flipping, during training. Additionally, a semi-automated segmentation and annotation strategy was developed using k-means clustering based on RGB color and shape features, generating binary masks that were further validated by a histopathologist. Both classification and segmentation models were implemented in Keras on a CPU-based environment with 32 GB RAM. We evaluated our models using scikit-learn. The classification achieved 79% accuracy on the 3-class test set and 90% accuracy on the binary test set (normal vs. invasive carcinoma), demonstrating competitive results compared to state-of-the-art models. The evaluation metrics used for both models were precision, recall, and F1-score; classification performance was further assessed using accuracy, and segmentation performance was evaluated using the Dice coefficient. Our classification model (DenseNet169 with partial fine-tuning) has approximately 4 million trainable parameters, while the segmentation model (U-Net with ResNet34 encoder) consists of around 25 million parameters—both models being compact enough for deployment on standard desktop CPUs. The segmentation masks were generated using a novel semi-automated pipeline that balances visual coherence (shape and structure) with RGB clustering, allowing more precise annotation than conventional thresholding or fully automated tools. These findings highlight that a selectively fine-tuned CNN can deliver reliable diagnostic assistance, even in resource- constrained environments.
Development of an Advanced Machine Learning Model for 3D Brain Segmentation in MRI
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Anam Amjad; CIIT/SP23-RCS-007/LHR; Dr. Zulfiqar Habib; LHR TP 9701
In the last few years, the rates of occurrence and death associated with brain tumors have shown a worrisome increase and now appear to be on a sustained uptick. The most recent report from the American Cancer Society (ACS) estimated that in 2020, 24,531 people in the United States would receive a diagnosis of a brain or other central nervous system tumor, and 18,306 would die from one of those tumors. The current research effort is a penetration into the brain tumor segmentation problem, and it seems to kick off with a quite elaborate preamble. To solve the problems of the heterogeneous tumors, the inconsistent morphology, and the deployment of clinical models, we propose a revised model of 3D Residual U-Net. This model has incorporated Convolutional Block Attention Modules (CBAM) a type of neural network component that allows it to better understand which pixels to pay attention to when outputting its results. We trained and evaluated this model on the BraTS benchmark 2020 dataset. The suggested framework attains high segmentation accuracy, along with computational efficiency, amassing Dice scores of 68% for ET, 83% for TC, and 91% for WT, strong generalization to the diverse, complex morphologies of brain tumors being evident here. By amending U-Net with residual learning, attention mechanisms, and a composite loss function (Dice + Focal), this framework provides a clinically viable approach that bridges the gap between research and real-world applicability in neuro- oncological imaging.

M.Phil / MS

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