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Various Image Modalities Used in Computer-Aided Diagnosis System for Detection of Breast Cancer Using Machine Learning Techniques: A Systematic Review

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Soft Computing and Signal Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1340))

Abstract

The detection of breast cancer using medical images popularly known as histopathology images often leads to a confusion and disagreement among pathologist. The most invasive cancer in females: breast cancer is affecting 12% for population worldwide. Breast cancer has no physical symptoms unless there is sign of painless lump. Breast cancer pathology images are classified into: normal, benign and malignant. Breast cancer cell has sub-classes interrelated to cells’ variability, organization and density with the structure of tissues and morphology. Main purpose of this systematic review is to investigate the performances of breast cancers diagnosis system using machine learning algorithm to find out the actual position of cancerous tissues in human body. We cover a different accentuation on the convolutional neural network (CNN) strategy for breast cancer image detection. Alongside with CNN various machine learning techniques, SVM, KNN, logistic regression and random forest are being used for breast cancer detection. Eventually, various studies were systematically reviewed, and the related articles published from 2010 to 2020 were considered. Result is calculated based on performance metric elements: accuracy, sensitivity, F1-score and area under curve. Best accuracy achieved so far is 98.9% using CNN classifier. This paper demonstrates various techniques as well as results of systematic review of breast cancer detection using CAD systems. Current statistics shows that different machine learning algorithm helps to improve detection and still requires more improvement and more flexibility to detect the breast cancer more accurately.

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Authors and Affiliations

  1. Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

    Gitanjali Wadhwa & Amandeep Kaur

Authors
  1. Gitanjali Wadhwa
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  2. Amandeep Kaur
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Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, India

    V. Sivakumar Reddy

  2. Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad, India

    V. Kamakshi Prasad

  3. Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ, USA

    Jiacun Wang

  4. Department of Electronics and Communication Engineering, Sir Visvesvaraya Institute of Technology, Nashik, India

    K. T. V. Reddy

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Wadhwa, G., Kaur, A. (2022). Various Image Modalities Used in Computer-Aided Diagnosis System for Detection of Breast Cancer Using Machine Learning Techniques: A Systematic Review. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol 1340. Springer, Singapore. https://doi.org/10.1007/978-981-16-1249-7_27

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