Browsing by Author "Ceylan, M."

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    Multi-View Thermal Breast Imaging for Malignancy Detection: Performance Benchmarking of CNN, Transformer, and Involution Architectures
    (Springer Science and Business Media Deutschland GmbH, 2026) Cihan, M.; Ceylan, M.
    Breast cancer screening demands accurate, non-invasive, low-cost tools. Infrared thermography is radiation-free and portable, but its utility hinges on robust computer-aided diagnosis (CAD). We benchmark three deep-learning families for static multi-view breast thermography—CNNs, Transformers, and an involution-based model (HarmonyNet-Lite). Experiments use the Breast Thermography dataset (119 patients; 476 manually segmented ROIs from anterior/oblique views). A compact pipeline performs ROI segmentation, RGB conversion, normalization, resizing, and moderate data augmentation; class imbalance is handled with minority oversampling and class-weighted loss. Evaluation follows patient-stratified five-fold cross-validation. HarmonyNet-Lite yields the best results: accuracy 87.47 ± 2.99%, recall 93.33 ± 2.13%, F1 68.43 ± 8.75%, and precision 54.23 ± 8.94%, indicating high sensitivity with an acceptable trade-off in precision for screening. Among CNNs, ResNet50 is strongest (85.59 ± 3.37%; F1 63.16 ± 3.87%), followed by InceptionV3 (83.38 ± 1.41%; F1 59.99 ± 6.72%), while DenseNet121 lags (79.25 ± 2.98%; F1 52.38 ± 5.62%). Transformer performance is mixed: ViT-Tiny is competitive (84.59 ± 4.23%; F1 59.46 ± 4.68%), whereas Swin-Tiny trails (81.30 ± 2.32%; F1 57.14 ± 4.44%) due to lower precision. Despite using only 0.14 M parameters, HarmonyNet-Lite outperforms heavier CNNs (ResNet50: 23.59 M; InceptionV3: 21.81 M) and lighter Transformers (ViT-Tiny: 2.84 M; Swin-Tiny: 11.78 M), demonstrating that content-adaptive, spatially aware involution operators efficiently capture fine thermal gradients. These findings position HarmonyNet-Lite as a strong, deployable CAD candidate. Future work will pursue multi-center validation, automated segmentation, multi-class labeling, hybrid involution–attention/multimodal models, and controlled GAN-based augmentation to mitigate data scarcity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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    Thermal Asymmetry in Football Players Following Ankle Injury: Findings Related to Training Load
    (Springer Science and Business Media Deutschland GmbH, 2026) Bayrak, A.; Çevik, M.; Ceylan, M.
    The Objective: This study aimed to evaluate, at two time points (the 1st and 14th training days), the effects of training provocation on thermal asymmetry in football players with and without a lateral ankle sprain (LAS) injury history. Methods: Twenty-seven football players from the U-19 squad of a Turkish Süper Lig club were included. Athletes were divided into two groups by injury history: with LAS injury history (n = 10) and without LAS injury history (n = 17). On the 1st and 14th training days, pre-training and post-training infrared thermographic images were obtained. For the ankle, patellar tendon, calf medialis, calf lateralis, and tibialis anterior regions, the side-to-side temperature difference (ΔT) and the post-training change (ΔPost–Pre) were calculated. Results: On the first day, athletes with LAS injury history showed a marked increase in ΔT at the ankle (+0.19 ℃) and patellar tendon (+0.22 ℃), whereas a decrease was observed in the control group. On the fourteenth day, ΔT values were elevated from pre-training in the injury-history group and expanded toward the calf muscles (calf medialis +0.07 ℃; calf lateralis +0.06 ℃). Tibialis anterior exhibited a decrease in both groups. Conclusion: In football players with LAS injury history, thermal asymmetry that emerges acutely at the joint–tendon level with training load extends to the muscle level over two weeks. This pattern indicates lasting alterations in neuromuscular control and load distribution. AI-assisted thermography can sensitively reveal such asymmetries and may serve as a valuable tool for individualized load management and injury prevention strategies during return-to-play. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.