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    Artificial Intelligence and Decision Support Applications in Liver Hydatid Disease: Detection, Classification, and Complication Prediction
    (Springer Science+Business Media, 2025) Karaduman, Mucahit; Yildirim, Muhammed; Akbulut, Sami
    Hydatid disease, caused by Echinococcus spp., is a parasitic infection commonly observed in endemic regions such as the Middle East, South America, and Central Asia. Imaging modalities like ultrasonography, computed tomography, and magnetic resonance imaging play a crucial role in diagnosing and managing this disease. However, these techniques have some limitations, particularly concerning diagnostic accuracy, operator dependency, and the inability to predict complications in advance. This chapter comprehensively addresses the use of artificial intelligence (AI) and clinical decision support systems in managing liver hydatid disease within this context. Since the disease most commonly affects the liver, the chapter specifically focuses on liver hydatid disease. AI-based technologies are increasingly utilized to overcome these challenges and optimize diagnostic processes. Deep learning algorithms (e.g., CNN, U-Net) have demonstrated high accuracy in analyzing imaging data. These algorithms enable the automated staging of liver hydatid cysts and predict the risk of complications. Notably, the automation of staging systems accelerates clinical decision-making and reduces discrepancies among expert opinions. Furthermore, surgical clinical decision support systems and complication prediction models not only enhance diagnostic processes but also make treatment planning more reliable. Despite the promising potential of AI models, their widespread clinical adoption faces obstacles such as the lack of high-quality data sets and the challenge of making model decisions interpretable. Therefore, multicenter studies and model validations based on extensive data sets are essential for integrating AI more effectively into clinical practice. In conclusion, AI-powered clinical decision support systems hold significant potential for standardizing and expediting the diagnostic and therapeutic processes in liver hydatid disease. However, further research is necessary to ensure their seamless integration into clinical practice. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
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    Content-Based Brain Magnetic Resonance Image Retrieval and Classification With the Proposed Deep Learning and Tissue-Based System
    (Ieee-Inst Electrical Electronics Engineers Inc, 2025) Dogan, Bedriye; Burak Mutlu, Hursit; Yildirim, Muhammed; Yalcin, Sercan; Aslan, Serpil; Sampathila, Niranjana; Yildirim, Ozal
    The exponential growth in the size of databases due to technological advancements has led to challenges in locating and accessing specific components of the data. While deep learning and other machine learning architectures have shown promise in retrieving data components, their efficacy is more pronounced when addressing disease cohorts. Contrarily, this effectiveness diminishes when accessing large datasets. This study focuses on the analysis of brain magnetic resonance imaging (MRI) images and, specifically, to differentiate between benign and malignant lesions associated with Alzheimer's disease, multiple sclerosis (MS), and intracranial regions, all of which are medically significant with distinct treatment modalities. A hybrid model was first devised to facilitate image retrieval by employing a pre-trained EfficientNet-b0 and local binary pattern (LBP) for feature extraction. These extracted features were then amalgamated to encompass diverse aspects of each image. To improve model performance, redundant features were pruned using the minimum redundancy maximum relevance (mRMR) technique. As a result, the proposed model demonstrated efficacy in analyzing a diverse dataset encompassing three distinct diseases and eight unique classes. Notably, existing machine architectures already published in the literature have struggled to achieve comparable success rates in discerning such closely related yet distinct disease groups. Our study underscores the challenge posed by increasing class diversity on the performance of deep learning architectures and obtained an accuracy of 98.9% in classifying three diseases and eight unique classes. As a result, the same model was used as the base in both the classification and CBIR processes for MRI detection, yielding competitive results when compared with the literature and other models.
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    Diagnosis and grading of vesicoureteral reflux on voiding cystourethrography images in children using a deep hybrid model
    (Elsevier Ireland Ltd, 2021) Eroglu, Yesim; Yildirim, Kadir; Cinar, Ahmet; Yildirim, Muhammed
    Background and objective: Vesicoureteral reflux is the leakage of urine from the bladder into the ureter. As a result, urinary tract infections and kidney scarring can occur in children. Voiding cystourethrography is the primary radiological imaging method used to diagnose vesicoureteral reflux in children with a history of recurrent urinary tract infection. Besides the diagnosis of reflux, it is graded with voiding cystourethrography. In this study, we aimed to diagnose and grade vesicoureteral reflux in Voiding cystourethrography images using hybrid CNN in deep learning methods. Methods: Images of pediatric patients diagnosed with VUR between 2016 and 2021 in our hospital (Firat University Hospital) were graded according to the international vesicoureteral reflux radiographic grading system. VCUG images of 236 normal and 992 with vesicoureteral reflux pediatric patients were available. A total of 6 classes were created as normal and graded 1-5 patients. Results: In this study, a hybrid-based mRMR (Minimum Redundancy Maximum Relevance) using CNN (Convolutional Neural Networks) model is developed for the diagnosis and grading of vesicoureteral re flux on voiding cystourethrography images. Googlenet, MobilenetV2, and Densenet201 models are used as a part of the hybrid architecture. The obtained features from these architectures are examined in concatenating process. Then, these features are classified in machine learning classifiers after optimizing with the mRMR method. Among the models used in the study, the highest accuracy value was obtained in the proposed model with an accuracy rate of 96.9%. Conclusions: It shows that the hybrid model developed according to the findings of our study can be used in the diagnosis and grading of vesicoureteral reflux in voiding cystourethrography images. (c) 2021 Elsevier B.V. All rights reserved.
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    Enhancing knee osteoarthritis detection with AI, image denoising, and optimized classification methods and the importance of physical therapy methods
    (Peerj Inc, 2025) Bugday, Burak; Bingol, Harun; Yildirim, Muhammed; Alatas, Bilal
    Osteoarthritis (OA) is considered one of the most challenging arthritic disorders due to its high disease burden and lack of effective treatment options that can change the course of the disease. Knee osteoarthritis (KOA) reduces people's quality of life and shortens their daily activities. Therefore, early detection of KOA dramatically impacts patients' quality of life. This study developed an artificial intelligence-supported system to detect KOA. In the developed system, firstly, the images in the original dataset were denoised with a Gaussian filter. Then, feature maps were extracted from both the original and Gaussian applied datasets with the DenseNet201 selected from eight different pre-trained models, and these two feature maps were concatenated. In this way, it is aimed to bring together different features of the same image. Then, feature selection was made using the neighborhood component analysis (NCA) method for the developed system to produce more successful results, and the optimized feature map was classified into six different classifiers. As a result, a high accuracy rate of 85% was achieved in the proposed model. This value is promising for the automatic diagnosis of KOA with computer-aided systems. As a result, a high accuracy rate of 85% was achieved in the developed system of the support vector machine (SVM) classifier. The proposed model was more successful than the other models used in the study.