Alam, Md AshrafulBhowmik, RonodeepRoy, AnweshaSaha, ShaptarshiOrpi, Miftahul JannatAhmed, Monjur2025-09-032025-09-0320252025-06ID 24141083ID 24141100ID 23341094ID 21101165ID 21301642http://hdl.handle.net/10361/26653Cataloged from PDF version of thesis.Includes bibliographical references (pages 70-74).This internship report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Business Administration, 2025.Cervical cancer continues to pose an enormous risk to global health, underscoring the urgent necessity for prompt and accurate detection to improve the health of women affected by this deadly illness. Current advancements on graph neural networks exhibit potential in classifying medical images, yet existing models usually demand massive computational loads, making their implementation unaffordable for clinically resource-limited settings. This research presents an intriguing ensemble learning framework that combines various graph neural network architectures, such as GraphMAE2(large and small variants), GAT, GIN, and GCN, aimed at improving the classification accuracy for cervical cancer identification. Moreover, our approach utilizes the colposcopy images, acquired from the dataset, and transforms them into graph representations enriched with handcrafted medical features and cervix-specific annotations. This provides a detailed and clinically contextualized description of the cervical area. Our ensemble model utilizes an innovative training approach featuring pre-training by masked autoencoding and fine-tuning with cross-validation to arrive at peak performance and generalizability on small datasets. The proposed ensemble model exhibits enhanced performance, attaining a validation accuracy of 95.52% and an F1 score of 94.12%, thereby exceeding the results of individual models. Additionally, we incorporate uncertainty quantification for enhancing the reliability of predictions and it provides insightful feedback on the confidence of predictions. This makes our framework an informative guidance tool for cervical cancer screening, with specific utility for clinically resource-limited settings. This contribution enhances the expanding domain of graph-based processing of medical images and delivers a robust and generalizable solution for healthcare systems.74 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Data imbalanceSuperpixelFeature maskingSelf-supervised learningGINClass weightElectronic data processing.Neural networks (Computer science).Cervix uteri--Cancer.Colposcopy.Thesis