Gaussian Aquila optimizer based dual convolutional neural networks for identification and grading of osteoarthritis using knee joint images

• Published in: Scientific reports Vol. 14; no. 1; pp. 7225 - 23
• Main Authors: Subha, B., Jeyakumar, Vijay, Deepa, S. N.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 27-03-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 692/700; Aquila optimizer; Arthritis; Classification; Deep learning; Dual convolutional neural network; Gaussian mutation; Humanities and Social Sciences; Humans; Knee; Knee Joint - diagnostic imaging; Knee-joint images; multidisciplinary; Musculoskeletal diseases; Neural networks; Neural Networks, Computer; Osteoarthritis; Osteoarthritis - diagnostic imaging; Pain; Science; Science (multidisciplinary); X-Ray images; Aquila optimizer; X-Ray images; Knee-joint images; Osteoarthritis; Dual convolutional neural network; Gaussian mutation
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-57002-4

Degenerative musculoskeletal disease known as Osteoarthritis (OA) causes serious pain and abnormalities for humans and on detecting at an early stage, timely treatment shall be initiated to the patients at the earliest to overcome this pain. In this research study, X-ray images are captured from the humans and the proposed Gaussian Aquila Optimizer based Dual Convolutional Neural Networks is employed for detecting and classifying the osteoarthritis patients. The new Gaussian Aquila Optimizer (GAO) is devised to include Gaussian mutation at the exploitation stage of Aquila optimizer, which results in attaining the best global optimal value. Novel Dual Convolutional Neural Network (DCNN) is devised to balance the convolutional layers in each convolutional model and the weight and bias parameters of the new DCNN model are optimized using the developed GAO. The novelty of the proposed work lies in evolving a new optimizer, Gaussian Aquila Optimizer for parameter optimization of the devised DCNN model and the new DCNN model is structured to minimize the computational burden incurred in spite of it possessing dual layers but with minimal number of layers. The knee dataset comprises of total 2283 knee images, out of which 1267 are normal knee images and 1016 are the osteoarthritis images with an image of 512 × 512-pixel width and height respectively. The proposed novel GAO-DCNN system attains the classification results of 98.25% of sensitivity, 98.93% of specificity and 98.77% of classification accuracy for abnormal knee case–knee joint images. Experimental simulation results carried out confirms the superiority of the developed hybrid GAO-DCNN over the existing deep learning neural models form previous literature studies.