Convolutional Neural Networks Using MobileNet for Skin Lesion Classification

被引：0

作者：

Sae-Lim, Wannipa ^{[1
]}

Wettayaprasit, Wiphada ^{[1
]}

Aiyarak, Pattara ^{[1
]}

机构：

[1] Prince Songkla Univ, Dept Comp Sci, Artificial Intelligence Res Lab, Hat Yai, Thailand

来源：

2019 16TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER SCIENCE AND SOFTWARE ENGINEERING (JCSSE 2019) | 2019年

关键词：

deep neural networks; convolutional neural networks; image processing; skin lesion classification; MobileNet; DEEP; SEGMENTATION; DIAGNOSIS;

D O I：

10.1109/jcsse.2019.8864155

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Skin lesion classification is a particular interesting area of research in dermatoscopic lesion image processing. In this paper, we present a skin lesion classification approach based on the light weight deep Convolutional Neural Networks (CNNs), called MobileNet. We employed MobileNet and proposed the modified MobileNet for skin lesion classification. For the evaluation of our model, we had used the official dataset of Human Against Machine with 10,000 training images (HAM 10000) which was a collection of multisource dermatoscopic images. Data up-sampling and data augmentation method were used in our study for improving the efficiency of the classifier. The comparison results showed that our modified model had achieved higher accuracy, specificity, sensitivity, and F1-score than the traditional MobileNet.

引用

页码：242 / 247

页数：6

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