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Explainable Artificial Intelligence with Scaling Techniques to Classify Breast Cancer Images

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Explainable Machine Learning for Multimedia Based Healthcare Applications

Abstract

According to the Breast Cancer Institute, one of the most hazardous diseases for women is breast cancer (BCI). Clinical specialists claim that detecting this malignancy early can save lives. More than 120 distinct tumors and related hereditary illnesses have individualized resources available, according to cancer.net. To diagnose breast cancer, machine learning techniques are mostly used. This research projects the use of eight machine learning (ML) approaches to accurately detect and classify breast cancer images as benign and malignant. Using Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, various scaling techniques were used on the eight different algorithms to build the projected model. Various libraries in Python programing language were used in Jupyter environments to achieve classification objectives. The results obtained from this implementation show that the Random Forest (RF) model gave the best classification results with 98.60% accuracy, 98.21% f1-score, 98.21% precision, 98.21% sensitivity, and false positive rate (FPR) of 1 on L2 scaling from other ML approaches implemented.

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Author information

Authors and Affiliations

  1. ECWA College of Nursing and Midwifery, Egbe, Kogi State, Nigeria

    Abdulwasiu Bolakale Adelodun

  2. Department of Multimedia Engineering, Kaunas University of Technology, Kaunas, Lithuania

    Roseline Oluwaseun Ogundokun

  3. Department of Computer Science, Landmark University, Omu Aran, Nigeria

    Roseline Oluwaseun Ogundokun

  4. Faculty of Electrical and Electronics Engineering, Kaunas University of Technology, Kaunas, Lithuania

    Akeem Olatunji Yekini

  5. Department of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin, Kwara State, Nigeria

    Joseph Bamidele Awotunde

  6. Mechatronics Engineering Programme, Bowen University, Iwo, Osun State, Nigeria

    Christopher Chiebuka Timothy

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  1. Abdulwasiu Bolakale Adelodun
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  2. Roseline Oluwaseun Ogundokun
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  3. Akeem Olatunji Yekini
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  4. Joseph Bamidele Awotunde
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  5. Christopher Chiebuka Timothy
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Editors and Affiliations

  1. Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    M. Shamim Hossain

  2. Department of Computer Engineering, Suleyman Demirel University, Isparta, Türkiye

    Utku Kose

  3. Maharaja Agrasen Institute of Technology, Delhi, India

    Deepak Gupta

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Adelodun, A.B., Ogundokun, R.O., Yekini, A.O., Awotunde, J.B., Timothy, C.C. (2023). Explainable Artificial Intelligence with Scaling Techniques to Classify Breast Cancer Images. In: Hossain, M.S., Kose, U., Gupta, D. (eds) Explainable Machine Learning for Multimedia Based Healthcare Applications. Springer, Cham. https://doi.org/10.1007/978-3-031-38036-5_6

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