scholarly journals Classification of Breast Cancer Histology Images Using Multi-Size and Discriminative Patches Based on Deep Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 21400-21408 ◽  
Author(s):  
Yuqian Li ◽  
Junmin Wu ◽  
Qisong Wu
Keyword(s):  
Breast Cancer ◽  
Deep Learning

A novel deep learning based framework for the detection and classification of breast cancer using transfer learning

2019 ◽  
Vol 125 ◽  
pp. 1-6 ◽  
Author(s):  
SanaUllah Khan ◽  
Naveed Islam ◽  
Zahoor Jan ◽  
Ikram Ud Din ◽  
Joel J. P. C Rodrigues
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Transfer Learning

A deep learning based approach for classification of CerbB2 tumor cells in breast cancer

2017 ◽  
Author(s):  
Gozde A. Tataroglu ◽  
Anil Genc ◽  
Kaan A. Kabakci ◽  
Abdulkerim Capar ◽  
B. Ugur Toreyin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Tumor Cells

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.

scholarly journals Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Jyoti Dabass ◽  
M. Hanmandlu ◽  
Rekha Vig
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Statistical Analysis ◽  
Fuzzy Set ◽  
State Of The Art ◽  
Entropy Function ◽  
Information Set ◽  
Learning Architectures

AbstractWith aim of detecting breast cancer at the early stages using mammograms, this study presents the formulation of five feature types by extending the information set to encompass the concept of an intuitionist fuzzy set. The resulting pervasive information set gives not only the certainty of the pixel intensities of mammograms to a class but also the deficiency in the fuzzy modeling referred to as the hesitancy. The generalized adaptive Hanman Anirban fuzzy entropy function is shown to be equivalent to the hesitancy entropy function. The probability-based fuzzy Hanman transform and the pervasive Information with probability taking the role of hesitancy degree help derive the above five feature types termed as probability-based pervasive Information set features. The effectiveness of each feature type is demonstrated on the mini-MIAS and DDSM databases for the multi-class categorization of mammograms using the Hanman transform classifier. The statistical analysis by ANOVA test proves that the features are statistically significant and the experimental results are shown to be clinically relevant by the expert radiologists. The performance of the five feature types is either superior to or equal to that of some deep learning architectures on comparison but they outperform the state-of-the-art literature methods in the classification of breast cancer using mammograms.

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