Acral melanoma detection using dermoscopic images and convolutional neural networks
| Autor: | Muhammad Usman Ghani, Qaiser Abbas, Farheen Ramzan |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
NC1-1940
Visual Arts and Performing Arts Computer applications to medicine. Medical informatics R858-859.7 Medicine (miscellaneous) Image processing Convolutional neural network Drawing. Design. Illustration QA76.75-76.765 Computer based diagnosis Medical image analysis Computer Science (miscellaneous) medicine Computer software business.industry Deep learning Melanoma Skin cancer detection Pattern recognition medicine.disease Computer Graphics and Computer-Aided Design Dermoscopic images Binary classification Acral melanoma Original Article Computer Vision and Pattern Recognition Artificial intelligence Skin cancer Transfer of learning business Software Convolutional networks |
| Zdroj: | Visual Computing for Industry, Biomedicine, and Art, Vol 4, Iss 1, Pp 1-12 (2021) Visual Computing for Industry, Biomedicine, and Art |
| ISSN: | 2524-4442 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink