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

Conference proceedings article

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Vakili, Naqibullah; Krathu, Worarat; Laomaneerattanaporn, Nongnuch;

ผู้เผยแพร่: Elsevier

ปีที่เผยแพร่ (ค.ศ.): 2021

หน้าแรก: 1

หน้าสุดท้าย: 7

จำนวนหน้า: 7

ISBN: 9781450390125

นอก: 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

ภาษา: English-Great Britain (EN-GB)

ดูบนเว็บไซต์ของสำนักพิมพ์

บทคัดย่อ

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.

คำสำคัญ

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