Custom Convolution Neural Network for Breast Cancer Detection
Thyagaraj T1, Keshava Prasanna2, Hariprasad S A3
1Thyagaraj T, Department of Electronics and Communication, BMS Institute of Technology and Management, Visvesvaraya Technological University, Belagavi, India.
2Keshava Prasanna, Department of Horticulture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga (Karnataka), India.
3Hariprasad S A, Faculty of Engineering and Technology, Jain Deemed to be University, Bengaluru (Karnataka), India.
Manuscript received on 24 November 2023 | Revised Manuscript received on 01 December 2023 | Manuscript Accepted on 15 December 2023 | Manuscript published on 30 December 2023 | PP: 22-29 | Volume-13 Issue-2, December 2023 | Retrieval Number: 100.1/ijeat.B43341213223 | DOI: 10.35940/ijeat.B4334.1213223
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Breast cancer remains a serious global health issue. Leveraging the use of deep learning techniques, this study presents a custom Convolutional Neural Network (CNN) framework for the detection of breast cancer. With the specific objective of accurate classification of breast cancer, a framework is made to analyze high-dimensional medical image information. The CNN’s architecture, which consists of specifically developed layers and activation components tailored for the categorization of breast cancer, is described in detail. Utilizing the Break His dataset, which comprises biopsy slide images of patients in a range of cancer stages, the model is trained and verified. Comparing our findings to conventional techniques, we find notable gains in sensitivity, specificity, and accuracy. Gray-Level Co-Occurrence Matrix (GLCM) features extracted from the Break His dataset was used to analyze the performance on sequential neural network, transfer learning and machine learning models. After analysis, we have proposed hybrid models of CNN-SVM, CNN-KNN, CNN-Logistic regression and achieved accuracy of about 95.2%
Keywords: Breast Cancer Detection, CNN, Mobile Net
Scope of the Article: Convolutional Neural Network