Esmez, OmerDeniz, GulnihalBilek, FurkanGurger, MuratBarua, Prabal DattaDogan, SengulTuncer, Turker2026-03-262026-03-2620252075-441810.3390/diagnostics151924782-s2.0-105019172959https://doi.org/10.3390/diagnostics15192478https://hdl.handle.net/20.500.14901/2806Dogan, Sengul/0000-0001-9677-5684; Deniz, Gülnihal/0000-0002-5944-8841; Baygin, Mehmet/0000-0001-6449-8950; Esmez, Omer/0000-0002-4475-3501; Barua, Prabal Datta/0000-0001-5117-8333; Gurger, Murat/0000-0002-7510-7203; Tuncer, Türker/0000-0002-5126-6445; Bilek, Furkan/0000-0003-1567-7201Background/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 (approximate to 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.eninfo:eu-repo/semantics/openAccessTurkernextv2Osteoarthritis DetectionDeep LearningPooling-Based AttentionBiomedical Image ClassificationArticle