A Deep learning Technique for Classification of Breast cancer Disease
Yelepi Usha Rani1, Lakshmi Sowmya Kotturi2, G.Sudhakar3
1Dr.Yelepi Usha Rani*, Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, India.
2Lakshmi Sowmya Kotturi, Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, India.
3Dr. G.Sudhakar, Assistant Professor, Department of Computer Science Engineering, School of Information Technology, JNTUH, India.
Manuscript received on October 21, 2021. | Revised Manuscript received on October 24, 2021. | Manuscript published on October 30, 2021. | PP: 09-14 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A31191011121 | DOI: 10.35940/ijeat.A3119.10110121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In recent years researchers are intensely using machine learning and employing AI techniques in the medical field particularly in the domain of cancer. Breast cancer is one such example and many studies have proposed CAD systems and algorithms to efficiently detect cancer cells and tumors. Breast cancer is one of the dreadful cancers accounting for a large portion of deaths caused due to cancer worldwide mostly affecting women, needs early detection for proper diagnosis, and subsequent decrease in death rate. Thus, for efficient classification, we implemented different ML techniques on Wisconsin dataset [1] namely SVM, KNN, Decision Tree, Random Forest, Naive Bayes using accuracy as a performance metric, and as per observance, SVM has shown better results when compared to other algorithms. Also, we worked on Breast Histopathology Images [2] scanned at 40x which had images of IDC which is one of the most common types of breast cancers. And to work with the image dataset along with EDA we used high-end techniques like a mobile net where smote a resampling was used to handle imbalanced class distribution, CNN, SVC, Inception Res Net V2 where frameworks like Tensor Flow, Keras were loaded for supporting the environment and smoothly implement the algorithms.
Keywords: Breast cancer, IDC (Invasive ductal cancer), Histopathology Images, Tensor Flow, Keras, CNN, SVC, InceptionResNetV2, SVM, KNN, Decision Tree, Random Forest, Naive Bayes, Wisconsin dataset.