Alam, Md. AshrafulSPARSHO, AVISHEK ROYSAHA, JOYEETASHOVON, MD. MUSFIQUR RAHMANAHAMED, MARZUKBIVA, ANONNA AMIN2025-02-242025-02-2420242024-12ID 20301269ID 20301087ID 20301332ID 20301169ID 20301382http://hdl.handle.net/10361/25548Cataloged from PDF version of thesis.Includes bibliographical references (pages 53-56).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024."Ocular Toxoplasmosis is considered a leading cause of visual impairment when not diagnosed and treated correctly on time. Its complications pose a challenge for accurate diagnosis and treatment. This study represents an efficient deep-learning approach for the multi-classification of Ocular Toxoplasmosis and its secondary complications using image datasets. In order to obtain a better fit of the model and further improve the data set, we incorporated its secondary complications. By carefully and accurately collecting a diverse set of data, we significantly enriched our custom dataset and enhanced its potential for insightful analysis. Deep learning techniques help us to develop an accurate multi-classification system for ocular toxoplasmosis and its associated complications. In this research, we utilized multiple CNN models and Transformer models, all of which demonstrated excellent accuracy. Additionally, we introduced two hybrid models by combining CNN and Transformer architectures. Finally, we also implemented robust ensemble architecture's two methods by combining the best-performing models: VGG19, ViT, and ResNet50. Furthermore, we integrated the XAI (GRAD-CAM) with the best accuracy 98% and F1 score 97% achiever Hybrid2(VGG19+ViT) model to enhance the understanding of our classification process and provide more transparency in the model’s decision-making."56 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.Ocular ToxoplasmosisDeep learningMulti-classificationFundus imagesOphthalmologyVisual impairmentCognitive learning theoryThesis