An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model

Al-rimy, Bander Ali Saleh and Saeed, Faisal and Al-Sarem, Mohammed and Albarrak, Abdullah M. and Qasem, Sultan Noman (2023) An Adaptive Early Stopping Technique for DenseNet169-Based Knee Osteoarthritis Detection Model. Diagnostics, 13 (11). p. 1903. ISSN 2075-4418

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Knee osteoarthritis (OA) detection is an important area of research in health informatics that aims to improve the accuracy of diagnosing this debilitating condition. In this paper, we investigate the ability of DenseNet169, a deep convolutional neural network architecture, for knee osteoarthritis detection using X-ray images. We focus on the use of the DenseNet169 architecture and propose an adaptive early stopping technique that utilizes gradual cross-entropy loss estimation. The proposed approach allows for the efficient selection of the optimal number of training epochs, thus preventing overfitting. To achieve the goal of this study, the adaptive early stopping mechanism that observes the validation accuracy as a threshold was designed. Then, the gradual cross-entropy (GCE) loss estimation technique was developed and integrated to the epoch training mechanism. Both adaptive early stopping and GCE were incorporated into the DenseNet169 for the OA detection model. The performance of the model was measured using several metrics including accuracy, precision, and recall. The obtained results were compared with those obtained from the existing works. The comparison shows that the proposed model outperformed the existing solutions in terms of accuracy, precision, recall, and loss performance, which indicates that the adaptive early stopping coupled with GCE improved the ability of DenseNet169 to accurately detect knee OA.

Item Type: Article
Identification Number:
25 May 2023Accepted
29 May 2023Published Online
Uncontrolled Keywords: knee OA detection, DenseNet169, early stopping, self-adaptive, GCE
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Gemma Tonks
Date Deposited: 21 Jun 2023 12:56
Last Modified: 21 Jun 2023 12:56

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