Beyond binary: multi-class skin lesion classification with AlexNet transfer learning-towards enhanced dermatological diagnosis
Abstract Problem Skin lesions are the major indicator for diagnosing different skin diseases, which are caused by the abnormal growth of skin cells. Skin cancer, one of the most fatal types of cancer in the world, relies on the proper diagnosis of skin lesions and other relevant disease indicators f...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-12-01
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| Series: | Discover Applied Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s42452-024-06448-2 |
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| Summary: | Abstract Problem Skin lesions are the major indicator for diagnosing different skin diseases, which are caused by the abnormal growth of skin cells. Skin cancer, one of the most fatal types of cancer in the world, relies on the proper diagnosis of skin lesions and other relevant disease indicators for early detection, which can enhance treatment and increase life expectancy by up to 92%. Diagnosing skin lesions accurately is challenging, even for experienced practitioners. Challenge Artificial Intelligence (AI) techniques based on deep learning have proved promising in the field of medical image processing and diagnosis. However, there is a lack of comprehensive medical image datasets. This paper will present a skin lesion classification model that employs a transfer learning approach based on Alex Net and classifies eight different skin diseases. This proposed model was trained, validated, and tested using ISIC 2019 challenge data with a very abnormal class distribution. This variation in class size has been overcome using data augmentation and resizing in the preprocessing phase. Preprocessing is performed in such a way that no colour detail or rich identification features will be lost from the images. Results The proposed model achieved a testing accuracy of 90.9%, with a precision of 90.8%, a sensitivity of 90.9%, and an F1-Score of 90.8%. The values for precision and sensitivity are among the highest reported, specifically for models using the ISIC 2019 dataset. |
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| ISSN: | 3004-9261 |