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ABSTRACT
Among the various cancer categories, breast cancer diagnoses are commonly found in many women, and automatic classification of breast cancer images is a crucial task using computer-aided analysis. This paper proposed a method for the detection of histopathological image-based breast cancer and the main motive of the technique is to employ feature extraction and classification based on DL techniques. The input image is filtered using edge smoothening for noise removal and image resizing on the pre-processing method and used 3D-Convolutional Neural network (3D_CNN) for extracting the features of the image process. Extracted deep features have been given as input for the classification process using Stochastic Diffusion Kernel Recursive Neural Networks (SDKRNN). The suggested model combines the sturdiness of convolutional and kernel blocks and demonstrates performance stability when compared to existing techniques. The experimental results are assessed using a variety of performance metrics and compared to current breast cancer detection methodologies. The result demonstrated the experimental evaluation analyzed for various datasets in terms of accuracy, precision, F-1 score, specificity, and AUC of 98%, 93.8%, 89%, 80%, and 70% and it determined the outperforms in the detection of breast cancer.
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No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
B. Buvaneswari
Dr. B. Buvaneswari has obtained her M.Tech degree in Computer Science and Engineering from Dr MGR Educational and Research Institute, Chennai in 2007 she has been working in the teaching field for about 21 years. She has guided many U.G. projects in the field of Computer Science and Engineering and she has published 10 papers in International Journals and 4 paper in international conferences. Her area of interest includes Image processing and Data Mining applications. Currently she is working as Professor in the Department of Technology at Panimalar Engineering College. She received Global Teacher Award 2019, AKS Education Awards at New Delhi and also received Best Teacher Award 2015 at Panimalar Engineering College.
J. Vijayaraj
Dr. J. Vijayaraj holds Ph.D. degree in Computer Science and Engineering from Pondicherry University, Pondicherry. His area of competence and interest includes Artificial Intelligence, IoT, Image Processing, Soft Computing and Machine Learning. He is Assistant Professor in Department of Artificial Intelligence and Data science at Easwari Engineering College, Chennai from last 1 years. At Easwari Engineering College, she has been teaching courses like Python Programming, Algorithm, Artificial Intelligence, Machine learning, Optimization etc.
B. Satheesh Kumar
Dr. B. Satheesh Kumar has received a Ph.D., in the Department of Computer Science and Engineering from Annamalai University, Chidambaram, India. He is working as an Assistant Professorin School of Computer Science and Engineering, Galgotias University, deemed to be University, Greater Noida, India.He received his Bachelor of Technology degree in the Department of Computer Science and Engineering from Arunai Engineering College, Thiruvanamalai,Tamilnaduin 2012; his Masters in Engineering in the Department of Computer Science and Engineering from PGP College of Engineering and Technology, Namakkal, Tamilnadu in 2014. His area of research includes, Digital Image Processing and Big Data Analytics, and Computer Vision, Machine learning and Video retrieval.He published article in SCI, SCIE, Scopus Journal and some International Conferences like IEEE, AISE, etc.He as 3 book chapters in IGI, CRC Press and Taylor and Francis and 3 book series in springer. He is also a reviewer in some reputed journals and as an external reviewer in conferences at IMPACT22.