Alam, Md. AshrafulFarooq, Md. OmarAlve, Md Azmain KhanKanta, Kaniz FatemaSarker, Md. RayhanHossain, Md Shahadat2025-09-152025-09-1520252025-06ID 20201154ID 20301399ID 22201671ID 20201165ID 20201181http://hdl.handle.net/10361/26733Cataloged from PDF version of thesis.Includes bibliographical references (pages 38-40).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Automatic classification of skin lesions from dermoscopic images plays a crucial role in early cancer detection. However, this task remains challenging due to visual similarities between lesion types, class imbalance, and image artifacts. While recent deep learning approaches, including ConvNeXt-based ensembles, have demonstrated impressive classification accuracy, their clinical reliability is hindered by uneven performance across classes, especially lower sensitivity in detecting high-risk conditions. In this study, we propose an efficient and interpretable framework based on a single ConvNeXt-Large model,trained and evaluated on the ISIC 2019 skin lesion dataset. Through systematic empirical optimization, we achieved a balanced performance that surpasses ensemble methods in clinically critical metrics, attaining a sensitivity of 86.27% and a specificity of 98.08% on the validation set. This was accomplished without compromising overall accuracy, while significantly reducing computational complexity compared to ensemble architectures. To enhance transparency and clinical trust, we further integrated Grad-CAM visual explanations, which reveal that the model consistently focuses on medically relevant lesion regions. Overall, this work contributes a practical, lightweight, and trustworthy deep learning solution for skin lesion classification that balances accuracy, efficiency, and interpretability—making it more suitable for real-world deployment.49 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.Skin lesion segmentationGradCAMISIC 2019 datasetImage analysisMedical imagesExplainable AIDeep learningSkin diseasesDisease detectionCancer identificationSkin--Diseases--Diagnosis.Skin--Cancer--Early detection.Deep learning (Machine learning).Artificial intelligence--Medical applications.Diagnostic imaging--Data processing.Thesis