Title: Breast cancer detection by fusion of deep features with CNN extracted features

Authors: Liang Zhou; Amita Nandal; Todor Ganchev; Arvind Dhaka

Addresses: Department of Radiology, Jiading District Central Hospital, No. 1, Chengbei Road, Jiading District, Shanghai, 201899, China; Affiliated to: Shanghai University of Medicine and Health Sciences, China ' Department of Computer and Communication Engineering, Manipal University Jaipur, India ' Department of Computer Science and Engineering, Technical University of Varna, Bulgaria ' Department of Computer and Communication Engineering, Manipal University Jaipur, India

Abstract: Breast cancer growth has become a typical factor nowadays. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerised features extraction and classification algorithms. In the recent times, breast cancer can be diagnosed by classifying tumours. In this paper, breast cancer identification and analysis is done by using machine learning statistical analysis. The proposed technique has proven to improve the exactness of foreseeing predicting cancer. The proposed method used optimised recording condition of the input image and later introduces a new interpretable feature for the identification. The simulation results are compared with conventional methods by using accuracy, sensitivity and specificity for performance assessment of the identification process.

Keywords: convolutional neural networks; CNNs; segmentation; tumours; machine learning algorithm; classification; image processing.

DOI: 10.1504/IJISTA.2022.128527

International Journal of Intelligent Systems Technologies and Applications, 2022 Vol.20 No.6, pp.510 - 523

Accepted: 25 Aug 2022
Published online: 25 Jan 2023 *

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