Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Cancer Diagnosis of Microscopic Biopsy Images Using Social Spider Optimization Tuned Neural Network

Version 1 : Received: 12 November 2021 / Approved: 15 November 2021 / Online: 15 November 2021 (10:40:52 CET)

How to cite: Balaji, P.; Chidambaram, K. Cancer Diagnosis of Microscopic Biopsy Images Using Social Spider Optimization Tuned Neural Network. Preprints 2021, 2021110246 (doi: 10.20944/preprints202111.0246.v1). Balaji, P.; Chidambaram, K. Cancer Diagnosis of Microscopic Biopsy Images Using Social Spider Optimization Tuned Neural Network. Preprints 2021, 2021110246 (doi: 10.20944/preprints202111.0246.v1).

Abstract

One of the most dangerous diseases that threaten people is Cancer. Cancer if diagnosed in earlier stages can be eradicated with its life threatening consequences. In addition, accuracy in prediction plays a major role. Hence, developing a reliable model that contributes much towards the medical community in early diagnosis of Biopsy images with perfect accuracy come to the scenario. The article aims towards development of better predictive models using multi-variate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimization (SSO) algorithm tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the NN classifier by the SSO algorithm. The performance of the proposed strategy is analysed with the performance metrics, such as accuracy, sensitivity, specificity, and MCC measures, and are attained to be 95.9181%, 94.2515%, 97.125%, and 97.68% respectively, which shows the effectiveness of the proposed method in effective cancer disease diagnosis.

Keywords

biopsy; cancer diagnosis; predictive models; neural network; optimization

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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