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AUTHOR(S): Ritik Ranjan
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ABSTRACT Drawing meaningful information from images is a crucial aspect of image processing. It is extensively utilised in several medicinal fields. Early diagnosis and treatment results, particularly in the case of cancer detection, can be greatly impacted by timely picture analysis. In view of the disease's rising global occurrence, this study investigates the diagnosis and treatment of breast cancer. Two important modalities—MRIs and mammograms—are used in the proposed approach to increase the accuracy of tumour identification. Numerous segmentation techniques, such as edge detection and threshold methods, are used to separate the tumorous regions. Several operators are also applied to the resulting images, and quantitative validation is performed using entropy measurements. Healthcare providers might potentially save lives and heal patients faster if breast cancer is discovered early on. |
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KEYWORDS image preprocessing, image, breast cancer, segmentation, mammogram, histogram analysis entropy, mri |
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Cite this paper Ritik Ranjan. (2024) Breast Cancer Detection Using Machine Learning. International Journal of Biology and Biomedicine, 9, 11-15 |
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