SMU Data Science Review
Abstract
Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new CNN. Deep neural networks are trained in a generative adversarial fashion in a semi-supervised environment by extracting low-level features that improve classification accuracy. This paper proposes an eloquent approach to classifying histopathological images accurately using Semi-Supervised GANs with a classification accuracy greater than 93%.
Recommended Citation
Avvaru, Balaji; Lohia, Nibhrat; Mani, Sowmya; and kaniti, Vijayasrikanth
(2022)
"Classification of Breast Cancer Histopathological Images Using Semi-Supervised GANs,"
SMU Data Science Review: Vol. 6:
No.
2, Article 13.
Available at:
https://scholar.smu.edu/datasciencereview/vol6/iss2/13
Included in
Categorical Data Analysis Commons, Data Science Commons, Diagnosis Commons, Medical Pathology Commons, Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Pathological Conditions, Signs and Symptoms Commons, Statistical Models Commons
