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    Detection of Acromion Types in Shoulder Magnetic Resonance Image Examination with Developed Convolutional Neural Network and Textural-Based Content-Based Image Retrieval System
    (Mdpi, 2025) Akcicek, Mehmet; Karaduman, Muecahit; Petik, Bulent; Unlu, Serkan; Mutlu, Hursit Burak; Yildirim, Muhammed
    Background: The morphological type of the acromion may play a role in the etiopathogenesis of various pathologies, such as shoulder impingement syndrome and rotator cuff disorders. Therefore, it is important to determine the acromion's morphological types accurately and quickly. In this study, it was aimed to detect the acromion shape, which is one of the etiological causes of chronic shoulder disorders that may cause a decrease in work capacity and quality of life, on shoulder MR images by developing a new model for image retrieval in Content-Based Image Retrieval (CBIR) systems. Methods: Image retrieval was performed in CBIR systems using Convolutional Neural Network (CNN) architectures and textural-based methods as the basis. Feature maps of the images were extracted to measure image similarities in the developed CBIR system. For feature map extraction, feature extraction was performed with Histogram of Gradient (HOG), Local Binary Pattern (LBP), Darknet53, and Densenet201 architectures, and the Minimum Redundancy Maximum Relevance (mRMR) feature selection method was used for feature selection. The feature maps obtained after the dimensionality reduction process were combined. The Euclidean distance and Peak Signal-to-Noise Ratio (PSNR) were used as similarity measurement methods. Image retrieval was performed using features obtained from CNN architectures and textural-based models to compare the performance of the proposed method. Results: The highest Average Precision (AP) value was reached in the PSNR similarity measurement method with 0.76 in the proposed model. Conclusions: The proposed model is promising for accurately and rapidly determining morphological types of the acromion, thus aiding in the diagnosis and understanding of chronic shoulder disorders.
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    Detection of osteochondral lesion of talus in ankle magnetic resonance images with GradCAM-based hybrid CNN model
    (Springer, 2026) Akcicek, Mehmet; Bingol, Harun; Petik, Bulent; Unlu, Serkan; Yildirim, Muhammed
    BackgroundTreatment of osteochondral lesion of talus (OLT), one of the crucial pathologies that can cause pain in the ankle, is guided by the age of onset, severity, and stage of the symptoms. For these reasons, early screening and early intervention for OLT become important. Magnetic resonance imaging (MRI) is used for further evaluation. However, depending on the clinician's experience, the diagnostic accuracy of the same images varies between physicians. In this study, we tried to determine the presence or absence of OLT using an AI-based hybrid model.MethodsThis study applied Gradient-Weighted Class Activation Mapping (GradCAM), an image visualization technique, to OLT images. Features were extracted and combined from the original and GradCAM-applied images. Then, the most valuable features from this high-dimensional feature map were selected using the Neighborhood Component Analysis (NCA) dimension reduction method. In the last stage, the feature map with selected features was classified in the K-Nearest Neighbors (KNN) classifier.ResultsTo compare the performance of our proposed model, feature extraction was performed with six pre-trained models accepted in the literature. These features were classified into six different classifiers. As a result, the proposed model achieved the highest success rate of 98.60%. The proposed hybrid model for detecting talus osteochondral lesions in ankle magnetic resonance images has obtained successful results. Thanks to this computer-aided system, experts' workload will be reduced, and this system can be used in places without experts.