cell carcinoma
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2022 ◽  
Vol 17 (3) ◽  
pp. 863-868
Author(s):  
Bao-Song NGUYEN-TRAN ◽  
Nam-Phuong TRAN-THI ◽  
Quy-Tran NGO ◽  
Lan LE-TRONG ◽  
Tung NGUYEN-THANH ◽  
...  

2022 ◽  
Vol 17 (3) ◽  
pp. 619-622
Author(s):  
Masashi Endo ◽  
Hiroyuki Fujii ◽  
Akifumi Fujita ◽  
Tatsuya Takayama ◽  
Daisuke Matsubara ◽  
...  

2022 ◽  
Vol 43 (2) ◽  
pp. 103303
Author(s):  
Gaelen B. Stanford-Moore ◽  
Ana Marija Sola ◽  
Jason Chan ◽  
Ivan El-Sayed ◽  
Jonathan George ◽  
...  

2022 ◽  
Vol 12 (5) ◽  
pp. 879-887
Author(s):  
Jiantao Zhang ◽  
Xiaobo Zhang ◽  
Dong Qu ◽  
Yan Xue ◽  
Xinling Bi ◽  
...  

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 that the system can correctly recognize 88.5% of patients with basal cell carcinoma and 86.5% of patients with Bowen’s disease.


Neoplasia ◽  
2022 ◽  
Vol 24 (2) ◽  
pp. 145-154
Author(s):  
Omar A. Saad ◽  
Wei Tse Li ◽  
Aswini R. Krishnan ◽  
Griffith C. Nguyen ◽  
Jay P. Lopez ◽  
...  

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