ArticleJournal of cancer research and clinical oncology2023
A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Classification of Renal Lesions by Leveraging Hybrid Features from CT Images Using Machine Learning Techniques.Journal of imaging informatics in medicine · 2026Article
- Intelligent breast cancer diagnosis with two-stage using mammogram images.Scientific reports · 2024Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
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4 authors.
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Abstract
purposeScreening 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.
methodsThe 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.
resultsThe results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%.
conclusionThe result indicates that the proposed approach can be applied to ensure breast cancer diagnosis.
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