International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 7 Issue 4
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Fuzzy Gompertz-Based Deep Ensemble with Explainable AI for Skin Lesion Classification

Author(s) Mostofa Rakib Raihan, Prof. Dr. Kamrul Hasan Talukder
Country Bangladesh
Abstract The skin cancer presents a formidable issue that requires prompt and precise diagnosis to ensure effective treatment. Analysis of medical imagery has been considerably enhanced by deep learning, particularly in the classification of skin disease. Deep ensemble approaches offer a compelling opportunity to further improve diagnostic accuracy. This research proposes an ensemble approach based on transfer learning techniques to achieve more precise outcomes. An ensemble model is created using ResNet50V2, DenseNet121 and MobileNetV2 for classifying skin lesions. Data augmentation methods were employed to enhance model accuracy by mitigating class imbalance. The final predictions are generated using the Gompertz function, which produces a fuzzy ranking of the base classifier models. The ensemble model shows an outstanding performance accuracy of 97.00% on HAM10000 dataset. The model's predictions were validated through Grad-CAM visualizations, revealing its focus on relevant lesion areas. These findings underscore that artificial intelligence-driven medical diagnostics can provide dependable and interpretable assistance for physicians, particularly in areas with reduced access to professional diagnostic tools.
Keywords Skin lesion classification, Transfer Learning, Ensemble learning, Gompertz function, Fuzzy ranking, Explainable AI, Medical Image Analysis.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-07-16
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.50601
Short DOI https://doi.org/g9tz2t
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