Huseyin Uvet

Rana Gursoy, Abdurrahim Yilmaz, Baris Kizilyaprak et al.

Dermatophytosis is commonly assessed using potassium hydroxide (KOH) microscopy, yet accurate recognition of fungal hyphae is hindered by artefacts, heterogeneous keratin clearance, and notable inter-observer variability. This study presents a transformer-based detection framework using the RT-DETR model architecture to achieve precise, query-driven localization of fungal structures in high-resolution KOH images. A dataset of 2,540 routinely acquired microscopy images was manually annotated using a multi-class strategy to explicitly distinguish fungal elements from confounding artefacts. The model was trained with morphology-preserving augmentations to maintain the structural integrity of thin hyphae. Evaluation on an independent test set demonstrated robust object-level performance, with a recall of 0.9737, precision of 0.8043, and an AP@0.50 of 93.56%. When aggregated for image-level diagnosis, the model achieved 100% sensitivity and 98.8% accuracy, correctly identifying all positive cases without missing a single diagnosis. Qualitative outputs confirmed the robust localization of low-contrast hyphae even in artefact-rich fields. These results highlight that an artificial intelligence (AI) system can serve as a highly reliable, automated screening tool, effectively bridging the gap between image-level analysis and clinical decision-making in dermatomycology.

Irem Sayin, Rana Gursoy, Buse Cicek et al.

This study develops a Convolutional Neural Network (CNN) model for detecting myocardial infarction (MI) from Electrocardiogram (ECG) images. The model, built using the InceptionV3 architecture and optimized through transfer learning, was trained using ECG data obtained from the Ch. Pervaiz Elahi Institute of Cardiology in Pakistan. The dataset includes ECG images representing four different cardiac conditions: myocardial infarction, abnormal heartbeat, history of myocardial infarction, and normal heart activity. The developed model successfully detects MI and other cardiovascular conditions with an accuracy of 93.27%. This study demonstrates that deep learning-based models can provide significant support to clinicians in the early detection and prevention of heart attacks.

Abdurrahim Yilmaz, Serra Atilla Aydin, Deniz Temur et al.

Mohs micrographic surgery (MMS) is the gold standard technique for removing high risk nonmelanoma skin cancer however, intraoperative histopathological examination demands significant time, effort, and professionality. The objective of this study is to develop a deep learning model to detect basal cell carcinoma (BCC) and artifacts on Mohs slides. A total of 731 Mohs slides from 51 patients with BCCs were used in this study, with 91 containing tumor and 640 without tumor which was defined as non-tumor. The dataset was employed to train U-Net based models that segment tumor and non-tumor regions on the slides. The segmented patches were classified as tumor, or non-tumor to produce predictions for whole slide images (WSIs). For the segmentation phase, the deep learning model success was measured using a Dice score with 0.70 and 0.67 value, area under the curve (AUC) score with 0.98 and 0.96 for tumor and non-tumor, respectively. For the tumor classification, an AUC of 0.98 for patch-based detection, and AUC of 0.91 for slide-based detection was obtained on the test dataset. We present an AI system that can detect tumors and non-tumors in Mohs slides with high success. Deep learning can aid Mohs surgeons and dermatopathologists in making more accurate decisions.

Hasan Berkay Abdioglu, Rana Gursoy, Yagmur Isik et al.

This study investigates the application of Super-Resolution techniques in holographic microscopy to enhance quantitative phase imaging. An off-axis Mach-Zehnder interferometric setup was employed to capture interferograms. The study evaluates two Super-Resolution models, RCAN and Real-ESRGAN, for their effectiveness in reconstructing high-resolution interferograms from a microparticle-based dataset. The models were assessed using two primary approaches: image-based analysis for structural detail enhancement and morphological evaluation for maintaining sample integrity and phase map accuracy. The results demonstrate that RCAN achieves superior numerical precision, making it ideal for applications requiring highly accurate phase map reconstruction, while Real-ESRGAN enhances visual quality and structural coherence, making it suitable for visualization-focused applications. This study highlights the potential of Super-Resolution models in overcoming diffraction-imposed resolution limitations in holographic microscopy, opening the way for improved imaging techniques in biomedical diagnostics, materials science, and other high-precision fields.

Serkan Salturk, Irem Sayin, Ibrahim Cem Balci et al.

Lumbar disk segmentation is essential for diagnosing and curing spinal disorders by enabling precise detection of disk boundaries in medical imaging. The advent of deep learning has resulted in the development of many segmentation methods, offering differing levels of accuracy and effectiveness. This study assesses the effectiveness of several sophisticated deep learning architectures, including ResUnext, Ef3 Net, UNet, and TransUNet, for lumbar disk segmentation, highlighting key metrics like as Pixel Accuracy, Mean Intersection over Union (Mean IoU), and Dice Coefficient. The findings indicate that ResUnext achieved the highest segmentation accuracy, with a Pixel Accuracy of 0.9492 and a Dice Coefficient of 0.8425, with TransUNet following closely after. Filtering techniques somewhat enhanced the performance of most models, particularly Dense UNet, improving stability and segmentation quality. The findings underscore the efficacy of these models in lumbar disk segmentation and highlight potential areas for improvement.

Abdurrahim Yilmaz, Mucahit Kalebasi, Yegor Samoylenko et al.

Skin cancer is one of the deadly types of cancer and is common in the world. Recently, there has been a huge jump in the rate of people getting skin cancer. For this reason, the number of studies on skin cancer classification with deep learning are increasing day by day. For the growth of work in this area, the International Skin Imaging Collaboration (ISIC) organization was established and they created an open dataset archive. In this study, images were taken from ISIC 2017 Challenge. The skin cancer images taken were preprocessed and data augmented. Later, these images were trained with transfer learning and fine-tuning approach and deep learning models were created in this way. 3 different mobile deep learning models and 3 different batch size values were determined for each, and a total of 9 models were created. Among these models, the NASNetMobile model with 16 batch size got the best result. The accuracy value of this model is 82.00%, the precision value is 81.77% and the F1 score value is 0.8038. Our method is to benchmark mobile deep learning models which have few parameters and compare the results of the models.

Abdurrahim Yilmaz, Ali Anil Demircali, Serra Ozkasap et al.

The purpose of this study is to find ideal forces for reducing cell stress in wound healing process by micro robots. Because of this aim, we made two simulations on COMSOL Multiphysics with micro robot to find correct force. As a result of these simulation, we created force curves to obtain the minimum force and friction force that could lift the cells from the surface will be determined. As the potential of the system for two micro robots that have 2 mm x 0.25 mm x 0.4 mm dimension SU-8 body with 3 NdFeB that have 0.25 thickness and diameter, simulation results at maximum force in the x-axis calculated with 4.640 mN, the distance between the two robots is 150 um.

Abdurrahim Yilmaz, Fatih Goktay, Rahmetullah Varol et al.

The diagnosis of superficial fungal infections in dermatology is still mostly based on manual direct microscopic examination with Potassium Hydroxide (KOH) solution. However, this method can be time consuming and its diagnostic accuracy rates vary widely depending on the clinician's experience. With the increase of neural network applications in the field of clinical microscopy, it is now possible to automate such manual processes increasing both efficiency and accuracy. This study presents a deep neural network structure that enables the rapid solutions for these problems and can perform automatic fungi detection in grayscale images without dyes. 160 microscopic field photographs containing the fungal element, obtained from patients with onychomycosis, and 297 microscopic field photographs containing dissolved keratin obtained from normal nails were collected. Smaller patches containing 4234 fungi and 4981 keratin were extracted from these images. In order to detect fungus and keratin, VGG16 and InceptionV3 models were developed. The VGG16 model had 95.98% accuracy, and the area under the curve (AUC) value of 0.9930, while the InceptionV3 model had 95.90% accuracy and the AUC value of 0.9917. However, average accuracy and AUC value of clinicians is 72.8% and 0.87, respectively. This deep learning model allows the development of an automated system that can detect fungi within microscopic images.

Abdurrahim Yilmaz, Asli Tekeci, Meryem Ece Ozyetkin et al.

This study investigates the effects of porosity on flying creatures such as dragonflies, moths, hummingbirds, etc. wing and shows that pores can affect wing performance. These studies were performed by 3D porous flapping wing flow analyses on Comsol Multiphysics. In this study, we analyzed different numbers of the porous wing at different angles of inclination in order to see the effect of pores on lift and drag forces. To compare the results 9 different analyses were performed. In these analyses, airflow velocity was taken as 5 m/s, angle of attack as 5 degrees, frequency as 25 Hz, and flapping angle as 30 degrees. By keeping these values constant, the number of pores was changed to 36, 48, and 60, and the pore angles of inclination to 60, 70, and 80 degrees. Analyses were carried out by giving laminar flow to this wing designed in the Comsol Multiphysics program. The importance of pores was investigated by comparing the results of these analyses.

Rahmetullah Varol, Sevde Omeroglu, Zeynep Karavelioglu et al.

Accurate assessment of stiffness distribution is essential due to the critical role of single cell mechanobiology in the regulation of many vital cellular processes such as proliferation, adhesion, migration, and motility. Cell stiffness is one of the fundamental mechanical properties of the cell and is greatly affected by the intracellular tensional forces, cytoskeletal prestress, and cytoskeleton structure. Herein, we propose a novel holographic single-cell stiffness measurement technique that can obtain the stiffness distribution over a cell membrane at high resolution and in real-time. The proposed imaging method coupled with acoustic signals allows us to assess the cell stiffness distribution with a low error margin and label-free manner. We demonstrate the proposed technique on HCT116 (Human Colorectal Carcinoma) cells and CTC-mimicked HCT116 cells by induction with transforming growth factor-beta (TGF-\b{eta}). Validation studies of the proposed approach were carried out on certified polystyrene microbeads with known stiffness levels. Its performance was evaluated in comparison with the AFM results obtained for the relevant cells. When the experimental results were examined, the proposed methodology shows utmost performance over average cell stiffness values for HCT116, and CTC-mimicked HCT116 cells were found as 1.08 kPa, and 0.88 kPa, respectively. The results confirm that CTC-mimicked HCT116 cells lose their adhesion ability to enter the vascular circulation and metastasize. They also exhibit a softer stiffness profile compared to adherent forms of the cancer cells. Hence, the proposed technique is a significant, reliable, and faster alternative for in-vitro cell stiffness characterization tools. It can be utilized for various applications where single-cell analysis is required, such as disease modeling, drug testing, diagnostics, and many more.