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Mammogram pectoral muscle removal and classification using histo-sigmoid based ROI clustering and SDNN

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Abstract

Mammograms are the images used by radiologists to diagnose breast cancer. Breast cancer is one of the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. One of the early screening methods of breast cancer that is still used today is mammograms due to their low cost. Unfortunately, this low cost accompanied by a low-performance rate also. Nowadays, the specific characterization of breast cancer images is a troublesome task. To overcome all the existing drawbacks, this research study develops a new algorithm for Mammogram Pectoral Muscle Removal using Histo-sigmoid based ROI Clustering and Classification using SDNN. Initially, the input breast image is first taken from the data set and pre-processed with wiener filtering. After that, Histo-sigmoid based ROI clustering is applied for the expulsion of the pectoral muscle. From that point, feature extraction using Hough transform and DCT. At last, the Support value-based adaptive deep neural network (SDNN) classifier clusters the mammogram pictures into normal, malignant, and benign classes accurately. Experimental results show that our proposed approach accomplishes the extreme classification accuracy outcome of MIAS Dataset is 99% and DDSM is 98%. Comparable to the MA_CNN, which achieves 96%. The SGR and Gestalt psychology had less Accuracy 94% and 94%.

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Authors and Affiliations

  1. Division of Computer Science and Engineering , School of Engineering, CUSAT, Kochi, India

    Girija O K & Sudheep Elayidom M.

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  1. Girija O K
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  2. Sudheep Elayidom M.
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O K, G., Elayidom M., S. Mammogram pectoral muscle removal and classification using histo-sigmoid based ROI clustering and SDNN. Multimed Tools Appl 81, 20993–21026 (2022). https://doi.org/10.1007/s11042-022-12599-4

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  • DOI: https://doi.org/10.1007/s11042-022-12599-4

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