Department of Mathematics
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Item Machine Learning Approaches in Medical Diagnosis(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Muhammad Bilal; CIIT/SP24-RMT-001/LHR; Dr. Muhammad Yousaf Bhatti; LHR TP 10065Brain tumors are among the most life-threatening neurological conditions, characterized by abnormal and uncontrolled cell growth within brain tissues. Early identification of tumor type and stage is crucial for effective intervention, yet conventional diagnostic methods often struggle to detect tumors in their early or subtle stages. This study proposes advanced deep-learning models for multi-class classification of brain tumors and machine-learning approaches for predicting chemical properties of anti-cancer drugs to support new drugs development. A hybrid deep-learning model was developed for classifying three tumor categories glioma, Meningioma and pituitary tumors using MRI scans. the model was trained on the brain tumor MRI dataset and integrates ResNet50 and Efficient Net50 as its base ar-chitectures, combined through a novel triangular dense-layer fusion strategy to optimize multi-stage feature extraction. The proposed model achieved a test accuracy of 97%, recall of 97%, and an AUC score of 99%, demonstrating its effectiveness for early and accurate brain-tumor classification. In second phase of this research, machine-learning algorithms and topological indices were used to analyze molecular structure of thirteen anti-cancer drugs. Topological in-dices were computed using a python program, and true physicochemical properties were retrieved from the ChemSpider database via an automated script. QSPR and SHAP anal-ysis were performed to identify indices most predictive of each physicochemical property. Machine-learning models were then trained on these features to develop generalized pre-dictive models for key durg properties, providing a foundation for computationally assisted drug discovery. Overall, this thesis demonstrates the combined potential of deep learning for early tu-mor detection and machine-learning for anti-cancer drug analysis, ultimately contributing to improved diagnostic accuracy and future therapeutic developmentItem Prediction of Lungs Cancer using Machine Learning Algorithms(Library Information Services COMSATS University Islamabad Lahore Campus, 2025) Muhammad Hamza FA23-RMT-055; Dr. Muhammad Yousaf Bhatti; LHR TP 9875The primary cause of death is lung cancer, primarily due to the uncontrolled growth of malignant tumors in the lungs that can spread to the body’s other organs, posing serious health risks. Smoking is a major contributing factor. Early detection is crucial to prevent this deadly disease. In order to detect lungs cancer early on, we want to develop deep learning and machine learning algorithms. Such a model would help physicians make informed diagnostic decisions and determine the appropriate level of diagnostic intensity for patients. This method has the potential to significantly reduce treatment costs by enabling physicians to tailor treatment plans based on precise predictions, thereby avoiding unnecessary and expensive procedures. Our aim is to establish a sustainable model that forecasts lung cancer affectively. Our findings indicate that ResNet-50 surpasses other models, achieving an accuracy rate of 76%. In comparison Support Vector Machine (SVM), Logistic Regression (LR), and EfficientNet-B0 achieved accuracies of 73%, 64%, and 55%, respectively. This research underscores the potential of leveraging computer technology and machine learning methods to increase the precision of lung cancer diagnoses from CT scans