Journal Article

Acral melanoma detection using dermoscopic images and convolutional neural networks

Ramzan, Farheen, Ghani, Muhammad Usman, Abbas, Qaiser

2021

Overview

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. In this study, we present a novel deep learning model, developed to classify skin cancer. We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions. 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, transfer learning was utilized to compare the performance of our model, where AlexNet and ResNet-18 were modified, fine-tuned, and trained on the same dataset. We achieved improved results from our proposed model with an accuracy of more than 90 % for AM and benign nevus, respectively. 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. From our analysis and results, we found that our model performed well and was able to effectively classify skin cancer. Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.

• Publisher: Springer Singapore,Springer Nature B.V,SpringerOpen
• Subject: Acral melanoma / Artificial neural networks / CAE) and Design / Cancer / Computer Graphics / Computer Imaging / Computer Science / Computer-Aided Engineering (CAD / Convolutional networks / Datasets / Decision making / Deep learning / Dermatology / Dermoscopic images / Diagnosis / Image classification / Image processing / Image Processing and Computer Vision / Lesions / Machine learning / Media Design / Medical image analysis / Medical imaging / Melanoma / Model accuracy / Original / Original Article / Pattern Recognition and Graphics / Skin cancer / Skin cancer detection / Vision