Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification

被引:8
作者
Ding, Jiaqi [1 ]
Song, Jie [1 ]
Li, Jiawei [1 ]
Tang, Jijun [2 ]
Guo, Fei [3 ]
机构
[1] Tianjin Univ, Sch Comp Sci & Technol, Coll Intelligence & Comp, Tianjin, Peoples R China
[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
[3] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China
来源
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY | 2022年 / 9卷
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
melanoma classification; ensemble learning; deep convolutional neural network; image segmentation; dermoscopy images; ABCD RULE; SEGMENTATION; IMAGES;
D O I
10.3389/fbioe.2021.758495
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Melanoma is a skin disease with a high fatality rate. Early diagnosis of melanoma can effectively increase the survival rate of patients. There are three types of dermoscopy images, malignant melanoma, benign nevis, and seborrheic keratosis, so using dermoscopy images to classify melanoma is an indispensable task in diagnosis. However, early melanoma classification works can only use the low-level information of images, so the melanoma cannot be classified efficiently; the recent deep learning methods mainly depend on a single network, although it can extract high-level features, the poor scale and type of the features limited the results of the classification. Therefore, we need an automatic classification method for melanoma, which can make full use of the rich and deep feature information of images for classification. In this study, we propose an ensemble method that can integrate different types of classification networks for melanoma classification. Specifically, we first use U-net to segment the lesion area of images to generate a lesion mask, thus resize images to focus on the lesion; then, we use five excellent classification models to classify dermoscopy images, and adding squeeze-excitation block (SE block) to models to emphasize the more informative features; finally, we use our proposed new ensemble network to integrate five different classification results. The experimental results prove the validity of our results. We test our method on the ISIC 2017 challenge dataset and obtain excellent results on multiple metrics; especially, we get 0.909 on accuracy. Our classification framework can provide an efficient and accurate way for melanoma classification using dermoscopy images, laying the foundation for early diagnosis and later treatment of melanoma.
引用
收藏
页数:12
相关论文
共 51 条
  • [1] [Anonymous], 2017, arXiv
  • [2] [Anonymous], 2017, Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble
  • [3] Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features
    Barata, Catarina
    Ruela, Margarida
    Francisco, Mariana
    Mendonca, Teresa
    Marques, Jorge S.
    [J]. IEEE SYSTEMS JOURNAL, 2014, 8 (03): : 965 - 979
  • [4] Bdair T.M., 2021, ABS210303703 CORR
  • [5] Multi-modality fusion learning for the automatic diagnosis of optic neuropathy
    Cao, Zheng
    Sun, Chuanbin
    Wang, Wenzhe
    Zheng, Xiangshang
    Wu, Jian
    Gao, Honghao
    [J]. PATTERN RECOGNITION LETTERS, 2021, 142 : 58 - 64
  • [6] Toward a combined tool to assist dermatologists in melanoma detection from dermoscopic images of pigmented skin lesions
    Capdehourat, German
    Corez, Andres
    Bazzano, Anabella
    Alonso, Rodrigo
    Muse, Pablo
    [J]. PATTERN RECOGNITION LETTERS, 2011, 32 (16) : 2187 - 2196
  • [7] A methodological approach to the classification of dermoscopy images
    Celebi, M. Emre
    Kingravi, Hassan A.
    Uddin, Bakhtiyar
    Lyatornid, Hitoshi
    Aslandogan, Y. Alp
    Stoecker, William V.
    Moss, Randy H.
    [J]. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2007, 31 (06) : 362 - 373
  • [8] A Transfer Learning Based Super-Resolution Microscopy for Biopsy Slice Images: The Joint Methods Perspective
    Chen, Jintai
    Ying, Haochao
    Liu, Xuechen
    Gu, Jingjing
    Feng, Ruiwei
    Chen, Tingting
    Gao, Honghao
    Wu, Jian
    [J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2021, 18 (01) : 103 - 113
  • [9] DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
    Chen, Liang-Chieh
    Papandreou, George
    Kokkinos, Iasonas
    Murphy, Kevin
    Yuille, Alan L.
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) : 834 - 848
  • [10] Discriminative Cervical Lesion Detection in Colposcopic Images With Global Class Activation and Local Bin Excitation
    Chen, Tingting
    Liu, Xuechen
    Feng, Ruiwei
    Wang, Wenzhe
    Yuan, Chunnv
    Lu, Weiguo
    He, Haizhen
    Gao, Honghao
    Ying, Haochao
    Chen, Danny Z.
    Wu, Jian
    [J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2022, 26 (04) : 1411 - 1421