Multiclass Skin Lesion Classification Using a Novel Lightweight Deep Learning Framework for Smart Healthcare

Applied Sciences

Ki-Ryong Kwon¹  Long Hoang¹  Suk-Hwan Lee²  Eung-Joo Lee³ 

[1] Department of Artificial Intelligence Convergence, Pukyong National University, Busan 48513, Korea;Department of Computer Engineering, Dong-A University, Busan 49315, Korea;Division of Artificial Intelligence, Tongmyong University, Busan 48520, Korea;

关键词: skin lesion classification;    medical image processing;    deep learning;    artificial intelligence;    big data;    wide-ShuffleNet;   

DOI  :  10.3390/app12052677

来源: DOAJ

【 摘 要 】

Skin lesion classification has recently attracted significant attention. Regularly, physicians take much time to analyze the skin lesions because of the high similarity between these skin lesions. An automated classification system using deep learning can assist physicians in detecting the skin lesion type and enhance the patient’s health. The skin lesion classification has become a hot research area with the evolution of deep learning architecture. In this study, we propose a novel method using a new segmentation approach and wide-ShuffleNet for skin lesion classification. First, we calculate the entropy-based weighting and first-order cumulative moment (EW-FCM) of the skin image. These values are used to separate the lesion from the background. Then, we input the segmentation result into a new deep learning structure wide-ShuffleNet and determine the skin lesion type. We evaluated the proposed method on two large datasets: HAM10000 and ISIC2019. Based on our numerical results, EW-FCM and wide-ShuffleNet achieve more accuracy than state-of-the-art approaches. Additionally, the proposed method is superior lightweight and suitable with a small system like a mobile healthcare system.

【 授权许可】

Unknown