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A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images

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Journal of Cancer Research and Clinical Oncology Aims and scope Submit manuscript

Abstract

Purpose

Screening programs use mammography as a diagnostic tool for the early detection of breast cancer. Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visible in the advanced image. For accurate diagnosis in the early stage of breast cancer, the appearance of masses and microcalcification on the mammographic image are two important indicators. The objective of this study was to evaluate the feasibility of the automatic separation of images of breast tissue microcalcifications and also to evaluate its accuracy.

Methods

The research was carried out by using two techniques of image enhancement and highlighting of breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method. After determining the clusters of breast tissue microcalcifications, the clusters are classified using the decision tree classification algorithm. Then, for segmentation, samples suspected of microcalcification are highlighted and masked, and in the last stage, tissue characteristics are extracted. Subsequently, with the help of an artificial neural network (ANN), determining the benign and malignant types of segmented ROI clusters was accomplished. The proposed system is trained with a Digital Database for Screening Mammography (DDSM) developed by the University of South Florida, USA, and the simulations are performed under MATLAB software and the results are compared with previous work.

Results

The results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%.

Conclusion

The result indicates that the proposed approach can be applied to ensure breast cancer diagnosis.

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Availability of data and materials

The data used to support the findings of this study are included within the article.

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Authors and Affiliations

  1. Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

    Zahra Maghsoodzadeh Sarvestani, Jasem Jamali, Mehdi Taghizadeh & Mohammad Hosein Fatehi Dindarloo

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  1. Zahra Maghsoodzadeh Sarvestani
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  2. Jasem Jamali
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  3. Mehdi Taghizadeh
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  4. Mohammad Hosein Fatehi Dindarloo
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Availability of data and materials: The data used to support the findings of this study are included within the article. Competing interest: The authors have no competing interests to declare that are relevant to the content of this article. Funding: No funding was received for conducting this study. Disclosure of potential conflicts of interest: the authors have no conflict of interest to declare.

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Correspondence to Jasem Jamali.

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Sarvestani, Z.M., Jamali, J., Taghizadeh, M. et al. A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images. J Cancer Res Clin Oncol 149, 6151–6170 (2023). https://doi.org/10.1007/s00432-023-04571-y

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