The Effects of Preprocessing Techniques on Nasnetmobile’s Performance for Classifying Knee Osteoarthritis Based on the Kellgren-Lawrence System
Abstract
Knee osteoarthritis (KOA) is a degenerative joint disorder characterized by the progressive deterioration of protective cartilage at the ends of bones, leading to pain and limited mobility. Deep learning provides an effective approach to classify whether X-ray images indicate the presence of KOA; however, dataset preprocessing techniques can enhance the efficacy of deep learning models. This study highlights the importance of preprocessing techniques in improving image contrast, particularly in utilizing the NASNetMobile model to assess the severity of KOA through X-ray images. KOA classification based on the Kellgren-Lawrence system consists of five severity levels; however, simplifying it into two categories can improve the performance of deep learning models. By fine-tuning parts of the NASNetMobile model and using the Nadam optimizer, the model initially achieved only 59% validation accuracy. However, by applying various preprocessing techniques, the model's validation accuracy improved to 80%.
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