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A novel machine learning model for breast cancer detection using mammogram images

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Abstract

The most fatal disease affecting women worldwide now is breast cancer. Early detection of breast cancer enhances the likelihood of a full recovery and lowers mortality. Based on medical imaging, researchers from all around the world are developing breast cancer screening technologies. Due to their rapid progress, deep learning algorithms have caught the interest of many in the field of medical imaging. This research proposes a novel method in mammogram image feature extraction with classification and optimization using machine learning in breast cancer detection. The input image has been processed for noise removal, smoothening, and normalization. The input image features were extracted using probabilistic principal component analysis for detecting the presence of tumors in mammogram images. The extracted tumor region is classified using the Naïve Bayes classifier and transfer integrated convolution neural networks. The classified output has been optimized using firefly binary grey optimization and metaheuristic moth flame lion optimization. The experimental analysis has been carried out in terms of different parameters based on datasets. The proposed framework used an ensemble model for breast cancer that made use of the proposed Bayes + FBGO and TCNN + MMFLO classifier and optimizer for diverse mammography image datasets. The INbreast dataset was evaluated using the proposed Bayes + FBGO and TCNN + MMFLO classifiers, which achieved 95% and 98% accuracy, respectively.

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Proposed architecture of mammogram image feature extraction with classification and optimization

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore, 641042, India

    P. Kalpana

  2. Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, 641032, India

    P. Tamije Selvy

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  1. P. Kalpana
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  2. P. Tamije Selvy
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Contributions

All authors agreed on the content of the study. PK and PTS collected all the data for analysis. PK agreed on the methodology. PK and PTS completed the analysis based on agreed steps. Results and conclusions are discussed and written together. The author read and approved the final manuscript.

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Correspondence to P. Kalpana.

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Kalpana, P., Selvy, P.T. A novel machine learning model for breast cancer detection using mammogram images. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03057-4

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