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Breast Tumor Classification Using Machine Learning Algorithm

Mohammed Shoaib, Sukkala Tharun Kumar Goud, V. Satya Bharathwaj, Y. Sreenivasulu

Abstract


Breast cancer is characterized by genetic mutations, periodic pain, changes in the length, color (redness) and texture of the breast skin. The categories of most types of breast cancer led the pathologist to a systematic and objective prognostic analysis. In general, the most common category is binary (benign/malignant). Today, Learning Gadget (ML) strategies are widely used in breast cancer classification problems. Offers extreme accuracy and powerful diagnostic technology. It kills more than any other disease, such as tuberculosis or malaria. In 2018, 17.1 million new instances of cancer were reported globally, according to the World Health Organization (WHO) Cancer Research and Reporting Agency. One of the four most prevalent cancers in women worldwide is breast cancer (that is, lungs, breast cancer, colon, etc.). According to studies, early detection and effective treatment of breast cancer significantly increase the likelihood of survival, and a proper diagnosis of benign tumors can spare patients from receiving needless care. As a result, there has been extensive research on the proper diagnosis of BC and the classification of patients into benign or malignant categories. To assist detect whether a breast tumor would be malignant or benign, this machine learning experiment uses a dataset. Various factors are considered, such as the thickness of the agglomerates, the number of bare nuclei, and mitosis. Machine learning (ML) has become an important part of medical imaging research, and recent advances have had a major impact on improving the diagnostic capabilities of CAD systems. The goal is to more accurately classify whether breast cancer is benign or malignant

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References


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