Classification of Breast Cancer Histopathology Images using Machine Learning Algorithms
Kavya K1, Savita K Shetty2
1Kavya K, Department of Software Engineering, M. S. Ramaiah Institute of Technology, Bengaluru, India.
2Savita K. Shetty, Assistant Professor, Department of Computer Science and Engineering M.S. Ramaiah Institute of Technology, Bengaluru, India.
Manuscript received on July 27, 2020. | Revised Manuscript received on August 10, 2020. | Manuscript published on August 10, 2020. | PP: 441-444 | Volume-9 Issue-10, August 2020 | Retrieval Number: 100.1/ijitee.J76030891020 | DOI: 10.35940/ijitee.J7603.0891020
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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: Machine Learning (ML), provides system the capacity to learn instinctively and allows systems to improve themselves with past experience and without being programmed specifically. In the field of Medical Science, ML plays important role. ML is being used to develop new practices in medical science which deals with huge patient data. Breast Cancer is a chronic disease commonly diagnosed in women. According to the survey by WHO, rank of breast cancer is at number one as compared to other cancers in female. BC has two kinds of tumour: Benign Tumour (BT), and Malignant Tumour (MT). BTs are treated as non-cancerous cells. MTs are treated as cancerous cells. The unidentified MTs in time stretch to other organs. Treatment procedure for BT and MT is different. So, it is salient to determine precisely whether a tumour is BT or MT. In this proposed model, Histopathology Images are used as dataset. These Histopathology images are pre-processed using Gaussian Blur and K-means Segmentation. The pre-processed data fed into feature extraction model. ML algorithms such as Support Vector Machine (SVM), Random Forest (RF) and Convolution Neural Network (CNN) are applied to extracted features. Performance of these algorithms is Analysed using accuracy, precision, recall and F1-score. CNN gives the highest accuracy with 87%.
Keywords: Machine Learning (ML), Breast Cancer (BC), Histopathology.
Scope of the Article: Machine Learning