A Deep Learning Approach for Basal Cell Carcinomas and Bowen's Disease Recognition in Dermatopathology Image

被引:1
作者
Zhang, Jiantao [1 ]
Zhang, Xiaobo [1 ]
Qu, Dong [1 ]
Xue, Yan [2 ]
Bi, Xinling [2 ]
Chen, Zhuo [3 ]
机构
[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China
[2] Second Mil Med Univ, Naval Med Univ, Changhai Hosp, Dept Dermatol, Shanghai 200433, Peoples R China
[3] Shanghai Jiao Tong Univ, Sch Med, Shanghai Childrens Med Ctr, Dept Dermatol, Shanghai 200127, Peoples R China
基金
上海市自然科学基金;
关键词
Deep Learning; CNN; Digital Pathology; Basal Cell Carcinoma; Bowen's Disease; NONMELANOMA SKIN-CANCER; RISK-FACTORS; DIAGNOSIS; CLASSIFICATION;
D O I
10.1166/jbt.2022.2982
中图分类号
Q813 [细胞工程];
学科分类号
摘要
Basal cell carcinomas and Bowen's disease (squamous cell carcinoma in situ) are the most common cutaneous tumors. The early diagnoses of these diseases are very important due to their better prognosis. But it is a heavy workload for the pathologists to recognize a large number of pathological images and diagnose these diseases. So, there is an urgent need to develop an automatic method for detecting and classifying the skin cancers. This paper presents a recognition system of dermatopathology images based on the deep convolutional neural networks (CNN). The dermatopathology images are collected from the hospital. The deep learning model is trained using different image datasets. It can be found that the recognition accuracy of the system can be improved by using data augmentation even if the number of the clinical samples are not increased. But the recognition accuracy of the system is the highest when the number of the original histological image is increased. The experimental results tat the system can correctly recognize 88.5% of IP: 49.249.253.194 On: Thu, 20 Jan 2022 09:17:08 patients with basal cell carcinoma and 86.5% of patients with Bowen's disease.
引用
收藏
页码:879 / 887
页数:9
相关论文
empty
未找到相关数据