A Study on Multiple Factors Affecting the Accuracy of Multiclass Skin Disease Classification

Jiayi Fan,Yongkeun Lee,Jongwook Kim,Insu Jung

doi:10.3390/app11177929

Jiayi Fan, Yongkeun Lee + Show 2 more

Open Access

https://doi.org/10.3390/app11177929

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Abstract

Diagnosis of skin diseases by human experts is a laborious task prone to subjective judgment. Aided by computer technology and machine learning, it is possible to improve the efficiency and robustness of skin disease classification. Deep transfer learning using off-the-shelf deep convolutional neural networks (CNNs) has huge potential in the automation of skin disease classification tasks. However, complicated architectures seem to be too heavy for the classification of only a few skin disease classes. In this paper, in order to study potential ways to improve the classification accuracy of skin diseases, multiple factors are investigated. First, two different off-the-shelf architectures, namely AlexNet and ResNet50, are evaluated. Then, approaches using either transfer learning or trained from scratch are compared. In order to reduce the complexity of the network, the effects of shortening the depths of deep CNNs are investigated. Furthermore, different data augmentation techniques based on basic image manipulation are compared. Finally, the choice of mini-batch size is studied. Experiments were carried out on the HAM10000 skin disease dataset. The results show that the ResNet50-based model is more accurate than the AlexNet-based model. The transferred knowledge from the ImageNet database helps to improve the accuracy of the model. The reduction in stages of the ResNet50-based model can reduce complexity while maintaining good accuracy. Additionally, the use of different types of data augmentation techniques and the choice of mini-batch size can also affect the classification accuracy of skin diseases.

Highlights

Artificial intelligence (AI), which has profoundly changed our everyday lives, has been extensively studied for several decades [1,2,3,4]
Some prominent applications are: AIempowered autonomous driving, which has been employed in numerous electric vehicles from various automakers such as Tesla and Ford [5]; AlphaGo developed by Google using artificial neural networks [6]; and the prevailing TikTok app, which has succeeded greatly due to its recommendation algorithms [7]
The transferred layers were frozen by assigning small learning rates to ensure only the newly added FC layer was trained and the features learned from the ImageNet database could be appropriately transferred

Summary

Introduction

Artificial intelligence (AI), which has profoundly changed our everyday lives, has been extensively studied for several decades [1,2,3,4]. An important aspect of AI is machine learning. Artificial neural networks, especially convolutional neural networks (CNNs), are extending the reach of machine learning to a broad range of applications [8,9]. The convolution kernels used in CNNs are extremely useful for the extraction of image features. With the aid of powerful graphic processing units, the classification of images has become efficient using deep CNN architectures. Numerous off-the-shelf architectures are available for use and can be adjusted to suit new tasks by using deep transfer learning [10,11]

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