Acral melanoma detection using dermoscopic images and convolutional neural networks

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. 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.

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images

“…The achieved accuracy for the Kaggle dataset was 88.95% and for the ISIC the accuracy was 90.96%. Abbas et al [ 33 ] introduced a custom build model for the classification of skin cancer based on the seven-layer of deep convolution network. The proposed model was trained from scratch.…”

Recent advances of artificial intelligence in melanoma clinical practice

“…The researchers collected 724 dermoscopic images from AM patients and benign nevus (BN) patients for training and validation and created a seven-layered deep CNN for training from the self-collected dataset. This model achieves an accuracy of 97% with a custom-built deep ConvNet for AM, and the accuracy can be improved by using TL [28]. Similarly, Yang et al .…”

Artificial Intelligence in Dermatology: Challenges and Perspectives

“…Finally, another study showed that the anatomic site of a lesion plays a critical role in the performance of CNN [ 38 ]. Studies [ 18 , 39 – 41 ] have shown the potential of CNN-based classification for special anatomic sites—such as face, palms, and soles that have different normal dermoscopic signs—but more extensive and diverse datasets as well as further research are needed to extend the application of AI in rare anatomic sites (e.g., genital area) and rare skin cancer subtypes (e.g., mucosal or desmoplastic MM) [ 17 ]. On the other side, banal-looking, benign lesions such as angiomas, dermatofibromas, or nevi are most often underrepresented or absent from studies’ training sets, leading to underperformance of the algorithms [ 18 ].…”