Version 1
: Received: 29 July 2024 / Approved: 31 July 2024 / Online: 1 August 2024 (03:28:13 CEST)
How to cite:
Badhon, S. M. S. I.; Khushbu, S. A.; Saha, N. C.; Anik, A. H.; Ali, M. A.; Hossain, K. T. Explainable AI for Skin Disease Classification Using Grad-CAM and Transfer Learning to Identify Contours. Preprints2024, 2024072556. https://doi.org/10.20944/preprints202407.2556.v1
Badhon, S. M. S. I.; Khushbu, S. A.; Saha, N. C.; Anik, A. H.; Ali, M. A.; Hossain, K. T. Explainable AI for Skin Disease Classification Using Grad-CAM and Transfer Learning to Identify Contours. Preprints 2024, 2024072556. https://doi.org/10.20944/preprints202407.2556.v1
Badhon, S. M. S. I.; Khushbu, S. A.; Saha, N. C.; Anik, A. H.; Ali, M. A.; Hossain, K. T. Explainable AI for Skin Disease Classification Using Grad-CAM and Transfer Learning to Identify Contours. Preprints2024, 2024072556. https://doi.org/10.20944/preprints202407.2556.v1
APA Style
Badhon, S. M. S. I., Khushbu, S. A., Saha, N. C., Anik, A. H., Ali, M. A., & Hossain, K. T. (2024). Explainable AI for Skin Disease Classification Using Grad-CAM and Transfer Learning to Identify Contours. Preprints. https://doi.org/10.20944/preprints202407.2556.v1
Chicago/Turabian Style
Badhon, S. M. S. I., Md. Aiyub Ali and K.S.M. Tozammel Hossain. 2024 "Explainable AI for Skin Disease Classification Using Grad-CAM and Transfer Learning to Identify Contours" Preprints. https://doi.org/10.20944/preprints202407.2556.v1
Abstract
This research evaluates the feasibility of addressing computer vision problems with limited resources, particularly in the context of medical data where patient privacy concerns restrict data availability. (1) Background: The study focuses on diagnosing skin diseases using five distinct transfer learning models based on convolutional neural networks. Two versions of the dataset were created, one imbalanced (4092 samples) and the other balanced (5182 samples), using simple data augmentation techniques. Preprocessing techniques were employed to enhance the quality and utility of the data, including image resizing, noise removal, and blur techniques. The performance of each model was assessed using fresh data after preprocessing. According to the research findings, (2) Methods: the VGG-19 model achieved an accuracy of 95.00% on the imbalanced dataset. After applying augmentation on the balanced data, the best-performing model was VGG-16-Aug with an accuracy of 97.07%. (3) Results: These results suggest that low-resource approaches, coupled with preprocessing techniques, can effectively identify skin diseases, particularly when utilizing the VGG-16-Aug model with a balanced dataset. (4) Conclusions: The study addresses rare skin disorders that have received limited attention in past research, including acne, vitiligo, hyperpigmentation, nail psoriasis, and SJS-TEN. The findings highlight the potential of simple data augmentation techniques moreover, explainable AI: Grad-CAM interpreted the model outcome by showing image contours visually as well as identifying uncommon skin conditions and overcoming the data scarcity challenge. The implications of these research findings are significant for the development of machine learning-based diagnostic systems in the medical field. Further investigation is necessary to explore the generalizability of these findings to other medical datasets.
Keywords
Skin disease; Transfer learning; VGG-16; CNN; Explainable AI; Grad-CAM
Subject
Public Health and Healthcare, Primary Health Care
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
