M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/52
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
Browse
2 results
Search Results
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 Use of Artificial Intelligence Approach For Visual Prediction & Transfer Learning(Library Information Services COMSATS University Lahore Campus, 2024-03-17) Hafiz Syed Muhammad Hur; FA22-RMT-037; Dr. Hafiz Muhammad Afzal Siddiqui; LHR TP 9381In this work, we introduce the identification of the MNIST database, which will be in handwritten digits that the machine can identify. The human handwriting form may be de tected and converted into computer language. We use several machine learning algorithms, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM). The MNIST database was developed using binary images of handwritten numbers (09) from NIST’s Special Database.Second, introduce the identification of TB photos. Tuber culosis is the biggest cause of mortality worldwide, according to the World Health Organi zation. Inadequate treatment and delayed or incorrect diagnosis have led to several cases of the illness. Accurate and timely diagnosis is critical for successfully managing and preventing tuberculosis. Despite significant progress in deep learning for medical image processing. There are two types of distributions: training and application data.Our findings show that transfer learning from a pre-trained vision transformer outperforms a pre-trained CNN in medical imaging