Fractional‐Harris hawks optimization‐based generative adversarial network for osteosarcoma detection using Renyi entropy‐hybrid fusion

International Journal of Intelligent SystemsVolume 36, Issue 10 p. 6007-6031 RESEARCH ARTICLE Fractional-Harris hawks optimization-based generative adversarial network for osteosarcoma detection using Renyi entropy-hybrid fusion Syed Jahangir Badashah, Corresponding Author Syed Jahangir Badashah [email protected] orcid.org/0000-0002-3658-1797 Sreenidhi Institute of Science and Technology (Autonomous), Yanampet, Ghatkesar, Hyderabad, Telangana, India Correspondence Syed Jahangir Badashah, Sreenidhi Institute of Science and Technology (Autonomous), Yanampet, Ghatkesar, Hyderabad 501301, Telangana, India.Email: [email protected]Search for more papers by this authorShaik Shafiulla Basha, Shaik Shafiulla Basha orcid.org/0000-0002-6463-8578 Y.S.R. Engineering College of Yogi Vemana University, Korrapadu Road, Proddatur, Andhra Pradesh, IndiaSearch for more papers by this authorShaik Rafi Ahamed, Shaik Rafi Ahamed orcid.org/0000-0003-4510-2708 Indian Institute of Technology Guwahati, Guwahati, Assam, IndiaSearch for more papers by this authorS. P. V. Subba Rao, S. P. V. Subba Rao orcid.org/0000-0001-8912-4103 Sreenidhi Institute of Science and Technology (Autonomous), Yanampet, Ghatkesar, Hyderabad, Telangana, IndiaSearch for more papers by this author Syed Jahangir Badashah, Corresponding Author Syed Jahangir Badashah [email protected] orcid.org/0000-0002-3658-1797 Sreenidhi Institute of Science and Technology (Autonomous), Yanampet, Ghatkesar, Hyderabad, Telangana, India Correspondence Syed Jahangir Badashah, Sreenidhi Institute of Science and Technology (Autonomous), Yanampet, Ghatkesar, Hyderabad 501301, Telangana, India.Email: [email protected]Search for more papers by this authorShaik Shafiulla Basha, Shaik Shafiulla Basha orcid.org/0000-0002-6463-8578 Y.S.R. Engineering College of Yogi Vemana University, Korrapadu Road, Proddatur, Andhra Pradesh, IndiaSearch for more papers by this authorShaik Rafi Ahamed, Shaik Rafi Ahamed orcid.org/0000-0003-4510-2708 Indian Institute of Technology Guwahati, Guwahati, Assam, IndiaSearch for more papers by this authorS. P. V. Subba Rao, S. P. V. Subba Rao orcid.org/0000-0001-8912-4103 Sreenidhi Institute of Science and Technology (Autonomous), Yanampet, Ghatkesar, Hyderabad, Telangana, IndiaSearch for more papers by this author First published: 21 July 2021 https://doi.org/10.1002/int.22539Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Osteosarcoma is the malignant bone sarcoma that is characterized by widespread genomic disruption and the inclination for metastatic spread. Early detection of osteosarcoma increases the survival rate. Various osteosarcoma detection methods are adopted to detect osteosarcoma at an early stage, but evaluating the slides under the microscope to find the degree of tumor necrosis and tumor result is a major challenge in the medical sector. Hence, an effective detection method is developed using the proposed Fractional-Harris Hawks Optimization-based Generative Adversarial Network (F-HHO-based GAN) for detecting osteosarcoma at an early stage. Here, the proposed F-HHO is designed by the integration of Fractional Calculus and HHO, respectively. Accordingly, the classification of viable tumor, nontumor, and the necrotic tumor is carried out by GAN using the histology image slides. GAN is used to perform osteosarcoma detection based on the features extracted from the image through the process of cell segmentation. The training process of GAN is done using the proposed F-HHO algorithm. However, the proposed F-HHO obtained better performance using the metrics, namely, accuracy, sensitivity, and specificity with the values of 98%, 98%, and 98% for training percentage and 96.282%, 97.552%, and 95.651% for K-fold, respectively. Volume36, Issue10October 2021Pages 6007-6031 RelatedInformation