#4474. Acral melanoma detection using dermoscopic images and convolutional neural networks

August 2026publication date
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Medicine (miscellaneous)
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Abstract:
Acral melanoma (AM) is a rare and lethal type of skin cancer. It can be diagnosed by expert dermatologists, using dermoscopic imaging. It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers. Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma. However, to date, limited research has been conducted on the classification of melanoma subtypes. The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes, such as, AM. Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection. Our custom-built model is a seven-layered deep convolutional network that was trained from scratch. Additionally, using the transfer learning approach, we achieved an average accuracy of nearly 97 %, which is comparable to that of state-of-the-art methods. Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.
Keywords:
Acral melanoma; Computer based diagnosis; Convolutional networks; Deep learning; Dermoscopic images; Medical image analysis; Skin cancer detection

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