A Breast Cancer Detection Using Image Processing and Machine Learning Techniques
Neela A G1, S Gayathri2, Jayashree K3

1Neela A G, Department of Electronics and Communication Engineering, JSS Academy of Technical Education, Bangalore, India.
2Dr. S Gayathri, Department of Electronics and Communication Engineering, JSS Science & Technological University, Mysuru, India.
3Dr. Jayashree K , Department of Pathology, JSS Research niversity, Mysuru, India.

Manuscript received on 13 August 2019. | Revised Manuscript received on 19 August 2019. | Manuscript published on 30 September 2019. | PP: 5250-5256 | Volume-8 Issue-3 September 2019 | Retrieval Number: C5906098319/2019©BEIESP | DOI: 10.35940/ijrte.C5906.098319
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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: Routine breast cancer screening allows the disease to be diagnosed and treated prior to it causing noticeable symptoms. During the diagnosis process there are chances of wrong detection hence a less human interfaced system has to be developed, hence the goal of breast cancer detection using machine learning techniques is used to find it before it spreads to the larger extent. Screening refers to tests and exams used to find a disease in people who don’t have any symptom. Early detection means finding and diagnosing a disease earlier than waiting for symptoms to start causing the effect on the neighboring cells. The breast cancer is the second most death causing cancer in humans, one in every ten women are affected by the breast cancer. Breast cancer is not only affecting the women. Men are also prone to get affected by the breast cancer but in smaller rates because of the absence of milk ducts and other lobules related to women. Early detection of the breast cancer helps in reducing the death rates if treated earlier and by proper diagnosis. In this paper the discussion of the various image processing technique done on the image and the CNN, SVM algorithm implementation on dataset images for the classification of malignant and non malignant cells are used and various tests were performed using different other machine learning algorithms and there level of accuracy and difference of various parameters are discussed for image processing MATLAB coding is used.
Keywords: Reoccurrence, Prediction, Classification, Breast, Non Reoccurrence, SVM, ANN, RNN ,CNN.

Scope of the Article: Classification