Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network

Lisheng Wei,Huosheng Hu,Kun Ding

doi:10.1109/access.2020.2997710

Lisheng Wei, Huosheng Hu + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2997710

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Abstract

The complex detection background and lesion features make the automatic detection of dermoscopy image lesions face many challenges. The previous solutions mainly focus on using larger and more complex models to improve the accuracy of detection, there is a lack of research on significant intra-class differences and inter-class similarity of lesion features. At the same time, the larger model size also brings challenges to further algorithm application; In this paper, we proposed a lightweight skin cancer recognition model with feature discrimination based on fine-grained classification principle. The propose model includes two common feature extraction modules of lesion classification network and a feature discrimination network. Firstly, two sets of training samples (positive and negative sample pairs) are input into the feature extraction module (Lightweight CNN) of the recognition model. Then, two sets of feature vectors output from the feature extraction module are used to train the two classification networks and feature discrimination networks of the recognition model at the same time, and the model fusion strategy is applied to further improve the performance of the model, the proposed recognition method can extract more discriminative lesion features and improve the recognition performance of the model in a small amount of model parameters; In addition, based on the feature extraction module of the proposed recognition model, U-Net architecture, and migration training strategy, we build a lightweight semantic segmentation model of lesion area of dermoscopy image, which can achieve high precision lesion area segmentation end-to-end without complicated image preprocessing operation; The performance of our approach was appraised through widespread experiments comparative and feature visualization analysis, the outcome indicates that the proposed method has better performance than the start-of-the-art deep learning-based approach on the ISBI 2016 skin lesion analysis towards melanoma detection challenge dataset.

Highlights

Skin cancer is one of with high mortality forms of cancer, according to cancer statistics released by the American Cancer Society, the mortality rate of patients with skin cancer is as high as 75% [1], [2], and the melanoma with the highest mortality rate is still increasing with an incidence of 14%
In this paper, we proposed an efficient and lightweight melanoma classification network based on MobileNet [46], DenseNet [47]
The related flowchart is shown in the figure below: The proposed recognition model uses two different features of the output of the lightweight CNN as the input of the feature discrimination networks to determine whether the two input images belong to the same type, so as to enhance the model ability to distinguish similar features between the melanoma and non-melanoma

Summary

Introduction

Skin cancer is one of with high mortality forms of cancer, according to cancer statistics released by the American Cancer Society, the mortality rate of patients with skin cancer is as high as 75% [1], [2], and the melanoma with the highest mortality rate is still increasing with an incidence of 14%. As a non-trauma skin imaging technique, dermoscopy is widespread used in the identification of melanoma. The accuracy of using dermoscopy to detect melanoma is higher than that without auxiliary observation [5], the diagnostic accuracy depends on the experience and professional skills of dermatologists. Even if dermatologists make the diagnosis, the accuracy of melanoma diagnosis can only reach 75-84% [6], and the diagnosis results of different doctors are different and have poor repeatability.

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