EFFECTIVE CLASSIFICATION OF KNEE OSTEOARTHRITIS USING MILITARY SCRUTOLF OPTIMIZATION-TUNED DCNN CLASSIFIER

Abstract

Accurately identifying the various types of knee osteoarthritis aids in an accurate diagnosis. The unique kind and severity of osteoarthritis enable medical specialists to offer the best management and treatment plans. Knee osteoarthritis greatly affects the living style of people by causing higher anxiety, mental issues, and health issues. Early treatment is possible because of early prediction, which may improve patient outcomes. Individuals may be able to prevent or postpone the development of knee osteoarthritis symptoms. An efficient categorization method for knee osteoarthritis employing the Military Scrutolf optimization-tuned deep Convolutional Neural Network (MSO-DCNN) and the advancement of study into this crippling disorder and the improvement of diagnosis, therapy, resource allocation, and disease monitoring are all made possible by the CNN classifier. The preprocessing of the data, which is carried out in three parts and involves the Circular Fourier Transform, Histogram Equalization, and Multivariate Linear Function, also contributes significantly to the success of this study. The proposed MSO technique, which improves convergence time and fine-tunes the classifier’s weight and Bias parameters, was built utilizing the features of military dogs and scrutolf to assist in getting increased seeking and hunting qualities. The MSO-tuned DCNN classifier’s adjusted weights and bias to give more effective desired classification results without using up more time or storage. By examining the performance measures and comparing the existing techniques to the MDO-based DCNN, the suggested MSO-DCNN improved based on TP accuracy by 1.33%, f1 measure by 2.9%, precision by 0.8%, and recall by 2.905%.