Multi-Class Primary Morphology Lesions Classification Using Deep Convolutional Neural Network

Conference proceedings article

Authors/Editors

WORARAT KRATHU

Strategic Research Themes

Data Science (Internet of Things)

Publication Details

Author list: Vakili, Naqibullah; Krathu, Worarat; Laomaneerattanaporn, Nongnuch;

Publisher: Elsevier

Publication year: 2021

Start page: 1

End page: 7

Number of pages: 7

ISBN: 9781450390125

ISSN: 0928-4931

eISSN: 1873-0191

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112226985&doi=10.1145%2f3468784.3468887&partnerID=40&md5=ee27e01318c6b13ff637bf4f4fa9711d

Languages: English-Great Britain (EN-GB)

View on publisher site

Abstract

Skin diseases are becoming the most prevalent health concern among all nations worldwide. Recognition of skin lesion, abnormal change usually caused by disease or infection in the skin is the first step in diagnosing skin diseases. In dermatology, morphology skin lesions occur due to the disease process's direct result and indicate categorizing a skin lesions' structure and appearance. In this work, we focus on primary skin lesion classification, particularly early-stage detection, and present a deep learning approach to classify images containing skin lesions, macule, nodule, papule, plaque pustule, wheal, and bulla. We applied deep learning techniques for classifying such images into seven classes covering the aforementioned types of lesion. In particular, we performed experiments on pre-trained deep convolutional neural network models to find the most accuracy one. The result shows that the pre-trained model ResNet-50 after the training and testing can achieve satisfactory accuracy of 85.95%. © 2021 ACM.

Keywords

Convolutional neural networks (CNN), Deep Model, Detection, Primary Lesions, ResNet-50, Skin disease, Transfer Learning