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

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 e...

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Bibliographic Details
Published inInternational journal of intelligent systems Vol. 36; no. 10; pp. 6007 - 6031
Main Authors Badashah, Syed Jahangir, Basha, Shaik Shafiulla, Ahamed, Shaik Rafi, Subba Rao, S. P. V.
Format Journal Article
LanguageEnglish
Published New York Hindawi Limited 01.10.2021
Subjects
Algorithms
Biomedical materials
Bone cancer
Entropy (Information theory)
Feature extraction
Fractional calculus
generative adversarial network
Generative adversarial networks
Harris hawks optimization
Histology
Image segmentation
Intelligent systems
Necrosis
Optimization
osteosarcoma
Renyi entropy
Training
Tumors
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Summary: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.
ISSN:0884-8173
1098-111X
DOI:10.1002/int.22539