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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of Computer and Information System (IJCIS)
Wiradinata, Marcell Jeremy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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The Effects of Preprocessing Techniques on Nasnetmobile's Performance for Classifying Knee Osteoarthritis Based on the Kellgren-Lawrence System Wiradinata, Marcell Jeremy; Wonohadidjojo, Daniel Martomanggolo
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8713

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%.
Co-Authors Budiono, Daniel Aprillio Daniel Martomanggolo Wonohadidjojo Utomo, Kevin Sander Wibowo, Kenny Jinhiro