A Combination Method of ROI, CLAHE, and DenseNet-169 for Hip Osteoarthritis Detection
Received: 15 February 2025 | Revised: 16 March 2025 | Accepted: 23 March 2025 | Online: 14 April 2025
Corresponding author: Faisal Muttaqin
Abstract
Hip osteoarthritis is the second most persistent type of osteoarthritis after the knee, and one of its most common symptoms is discomfort in the afflicted joint. A previous study stated that 87.83% of patients undergoing hip arthroplasty were 60 years or older, with a higher prevalence among women, reflected in a 2:1 female-to-male ratio. Image enhancement is one of the most significant aspects of image processing, which, in addition to enhancing the clarity of visual images, is helpful for further analysis in the field of computer processing. This study employs a combination of techniques, specifically ROI (Region of Interest), CLAHE (Contrast Limited Adaptive Histogram Equalization), and DenseNet-169. ROI is used to identify and define the specific area or object to be segmented. CLAHE is used to improve contrast in images, particularly those in grayscale, and DenseNet-169 is used for the detection of hip osteoarthritis. A total of 750 hip X-ray images were divided into 3 groups: 250 for normal osteoarthritis, 250 for mild osteoarthritis, and 250 for severe osteoarthritis. The proposed model obtained better scores than previous ones, with the following results: accuracy of 98.67%, precision of 98.70%, recall of 98.67%, and F1-score of 98.66%. These results show that the integration of ROI and CLAHE preprocessing techniques with DenseNet-169 effectively identifies hip osteoarthritis.
Keywords:
image enhancement, deep learning, osteoarthritis, CLAHE, ROI, DenseNet-169Downloads
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Copyright (c) 2025 Faisal Muttaqin, Paulus Rahardjo, Muhammad Zaim Chilmi, Athanasius Priharyoto Bayuseno, Tri Indah Winarni, R. Rizal Isnanto, Jamari
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