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Diagnostics
Volume 15
Issue 19
10.3390/diagnostics15192478
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

TurkerNeXtV2: An Innovative CNN Model for Knee Osteoarthritis Pressure Image Classification

by
Omer Esmez
1,
Gulnihal Deniz
2,
Furkan Bilek
3,
Murat Gurger
4,
Prabal Datta Barua
5,
Sengul Dogan
6,*,
Mehmet Baygin
7 and
Turker Tuncer
6
1
Department of Orthopedics, Elazig Fethi Sekin City Hospital, Elazig 23280, Turkey
2
Department of Physiotherapy and Rehabilitation, Faculty of Health Sciences, Erzurum Technical University, Erzurum 25050, Turkey
3
Department of Gerontology, Fethiye Faculty of Health Sciences, Mugla Sitki Kocman University, Mugla 48000, Turkey
4
Department of Orthopedics, Firat University Hospital, Firat University, Elazig 23119, Turkey
5
School of Business (Information System), University of Southern Queensland, Toowoomba, QLD 4350, Australia
6
Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig 23119, Turkey
7
Department of Computer Engineering, Erzurum Technical University, Erzurum 25050, Turkey
*
Author to whom correspondence should be addressed.
Diagnostics 2025, 15(19), 2478; https://doi.org/10.3390/diagnostics15192478 (registering DOI)
Submission received: 4 August 2025 / Revised: 22 September 2025 / Accepted: 24 September 2025 / Published: 27 September 2025
(This article belongs to the Special Issue Advances in Machine Learning for Medical Image Processing and Analysis)
Versions Notes

Abstract

Background/Objectives: Lightweight CNNs for medical imaging remain limited. We propose TurkerNeXtV2, a compact CNN that introduces two new blocks: a pooling-based attention with an inverted bottleneck (TNV2) and a hybrid downsampling module. These blocks improve stability and efficiency. The aim is to achieve transformer-level effectiveness while keeping the simplicity, low computational cost, and deployability of CNNs. Methods: The model was first pretrained on the Stable ImageNet-1k benchmark and then fine-tuned on a collected plantar-pressure OA dataset. We also evaluated the model on a public blood-cell image dataset. Performance was measured by accuracy, precision, recall, and F1-score. Inference time (images per second) was recorded on an RTX 5080 GPU. Grad-CAM was used for qualitative explainability. Results: During pretraining on Stable ImageNet-1k, the model reached a validation accuracy of 87.77%. On the OA test set, the model achieved 93.40% accuracy (95% CI: 91.3–95.2%) with balanced precision and recall above 90%. On the blood-cell dataset, the test accuracy was 98.52%. The average inference time was 0.0078 s per image (≈128.8 images/s), which is comparable to strong CNN baselines and faster than the transformer baselines tested under the same settings. Conclusions: TurkerNeXtV2 delivers high accuracy with low computational cost. The pooling-based attention (TNV2) and the hybrid downsampling enable a lightweight yet effective design. The model is suitable for real-time and clinical use. Future work will include multi-center validation and broader tests across imaging modalities.
Keywords: TurkerNeXtV2; osteoarthritis detection; deep learning; pooling-based attention; biomedical image classification TurkerNeXtV2; osteoarthritis detection; deep learning; pooling-based attention; biomedical image classification

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MDPI and ACS Style

Esmez, O.; Deniz, G.; Bilek, F.; Gurger, M.; Barua, P.D.; Dogan, S.; Baygin, M.; Tuncer, T. TurkerNeXtV2: An Innovative CNN Model for Knee Osteoarthritis Pressure Image Classification. Diagnostics 2025, 15, 2478. https://doi.org/10.3390/diagnostics15192478

AMA Style

Esmez O, Deniz G, Bilek F, Gurger M, Barua PD, Dogan S, Baygin M, Tuncer T. TurkerNeXtV2: An Innovative CNN Model for Knee Osteoarthritis Pressure Image Classification. Diagnostics. 2025; 15(19):2478. https://doi.org/10.3390/diagnostics15192478

Chicago/Turabian Style

Esmez, Omer, Gulnihal Deniz, Furkan Bilek, Murat Gurger, Prabal Datta Barua, Sengul Dogan, Mehmet Baygin, and Turker Tuncer. 2025. "TurkerNeXtV2: An Innovative CNN Model for Knee Osteoarthritis Pressure Image Classification" Diagnostics 15, no. 19: 2478. https://doi.org/10.3390/diagnostics15192478

APA Style

Esmez, O., Deniz, G., Bilek, F., Gurger, M., Barua, P. D., Dogan, S., Baygin, M., & Tuncer, T. (2025). TurkerNeXtV2: An Innovative CNN Model for Knee Osteoarthritis Pressure Image Classification. Diagnostics, 15(19), 2478. https://doi.org/10.3390/diagnostics15192478

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