scholarly journals Improved Classification of Cancerous Histopathology Images using Color Channel Separation and Deep Learning

2021 ◽  
Vol 8 (3) ◽  
pp. 175-182
Author(s):  
Rachit Kumar Gupta ◽  
Jatinder Manhas
Keyword(s):  
Deep Learning ◽  
Color Channel ◽  
Histopathology Images ◽  
Channel Separation

scholarly journals Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4373 ◽  
Author(s):  
Zabit Hameed ◽  
Sofia Zahia ◽  
Begonya Garcia-Zapirain ◽  
José Javier Aguirre ◽  
Ana María Vanegas
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Diagnostic System ◽  
Microscopic Analysis ◽  
Classification Performance ◽  
Histopathological Analysis ◽  
Learning Approach ◽  
Ensemble Strategy ◽  
Histopathology Images

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.

Deep Learning-Based Classification of Liver Cancer Histopathology Images Using Only Global Labels

2020 ◽  
Vol 24 (6) ◽  
pp. 1643-1651 ◽  
Author(s):  
Chunli Sun ◽  
Ao Xu ◽  
Dong Liu ◽  
Zhiwei Xiong ◽  
Feng Zhao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Liver Cancer ◽  
Histopathology Images

Classification of Customer to Upgrade Profits in Retail Market with Deep Learning Methodology

2019 ◽  
Vol 7 (5) ◽  
pp. 188-191
Author(s):  
I.Gayathri Devi ◽  
G. Surya Kala Eswari ◽  
G. Kumari
Keyword(s):  
Deep Learning ◽  
Retail Market
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