Open AccessArticle
Computationally Efficient Explainable AI Framework for Skin Cancer Detection
Author’s Name: Kohinur Parvin 1
, Eshat Ahmad Shuvo* 2, Wali Ashraf Khan 3, Sakibul Alam Adib 3, Tahmina Akter Eiti 4, Mohammad Shovon 1, Shoeb Akter Nafiz 5
, Eshat Ahmad Shuvo* 2, Wali Ashraf Khan 3, Sakibul Alam Adib 3, Tahmina Akter Eiti 4, Mohammad Shovon 1, Shoeb Akter Nafiz 51 Department of Computer Science and Engineering, Netrokona University, Netrokona-2400, Bangladesh
2 Department of Computer Science and Engineering, Bangladesh University, Dhaka-1207, Bangladesh
3 Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
4 Department of Computer Science and Engineering, Siddheswari College, Dhaka-1217, Bangladesh
5 Department of Computer Science & Engineering, Daffodil International University, Dhaka-1216, Bangladesh
*whom correspondence should be addressed. E-mail: mdshuvoslk012@gmail.com
Adv. Sci. Technol. Eng. Syst. J. 11(1), 11–24 (2026);
DOI: 10.25046/aj110102
Received: 17 October 2025, Revised: 18 December 2025, Accepted: 20 December 2025, Published Online: 10 January 2026
(This article belongs to the ADAIS26 (Special Issue on Advances in Data-Driven Analytics and Intelligent Systems 2026) & Section Artificial Intelligence in Computer Science (CAI))
Skin cancer stands among some of the fastest growing and fatal malignancies of the world as a result early and accurate diagnosis of skin cancer is essential in order to enhance patient survival and treatment prognosis. Conventional methods of diagnosis including dermoscopy and histopathological examinations are expensive and time consuming also subject to inter-observer error. To address these shortcomings, this research suggests an optimized hybrid cascading framework that combines deep and machine learning strategies towards building an AI model for the detection of skin cancer. This study applied five different Convolutional Neural Network (CNN) as well as seven Machine Learning (ML) model and three optimization technique. Among them the suggested MobileNetV2 + LDA + LR model had the best accuracy of 99.33%, precision of 99.47%, recall of 97.33%, and F1-score of 98.31%. Moreover, it was found that the framework had better computational efficiency, with a time complexity of 35.1 ± 1.24 seconds and moderate memory usage. Later on, Explainable AI (XAI) including Grad-CAM++ and LIME techniques were employed to validate the interpretability of the model and revealed the clinically relevant areas of lesions that affected predictions, which increased the level of transparency and credibility.
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