High Accuracy Microcalcifications Detection of Breast Cancer Using Wiener LTI Tophat Model

In order to avoid cancer, it is imperative that microcalcification in the breast be found. It is sufficiently small to be difficult to discern with the unassisted eye. Computer-based detection output is modest and tends to stay concealed from the radiologist doing the examination, which might help the radiologist increase diagnostic accuracy. According to this study, the best Wiener Linear Time Invariant Filter method with Tophat Transformation (LFWT) can identify microcalcification in the breast with an accuracy rate of 99.5%. In this work, we focused on the identification of microcalcifications in images, an essential initial step towards precisely identifying all the indicators in a mammography-based early breast cancer diagnosis. To make the cancer region visible and prominent, the Wiener and CLAHE filters are used. Tophat morphological operators were applied to mask detection, and edges were extracted. The analytical performance of the proposed model for microcalcification identification in mammograms was evaluated and compared with other approaches using Mammographic Image Analysis Society (MIAS) and Mini-Mammographic imaging datasets. Additionally, three techniques- The Local Contrast Method (LCM), the Local Relative Contrast Measure Method (LRCMM), and the High-Boost-Based Multiscale Local Contrast Measure (HBBMLCM) are used to identify microcalcification linked to cancer on mammography images. Performance Evaluation of the Proposed Model: the LFWT methodology had the best level of efficacy in detecting microcalcification linked to breast cancer. The suggested LFWT technique finds each and every tiny point on the MIAS dataset's mammography.