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Öğe A hybrid ViT-CNN model with attention mechanism and dual feature selection for cervical cancer detection(Springer London Ltd, 2025) Onal, Merve Kesim; Van, Abdullah Enes; Avci, Derya; Yildirim, Muhammed; Bingol, Harun; Avci, EnginCervical cancer is the most common and fatal disease encountered by women worldwide. Early diagnosis of cervical cancer plays a critical role in reducing mortality rates and initiating the treatment process early. In this study, a hybrid model was developed to detect cervical cancer with high accuracy. In the developed model, 1280 and 768 features were extracted from each image, respectively, using pre-trained EfficientNet V2-M and Vision Transformer (ViT) architectures as the base; these features were combined to obtain a combined feature vector of 2048 dimensions. A feature attention mechanism was applied to highlight the important information in the data input, and then dimensionality reduction was performed using the mRMR and NCA methods. 838 standard features between the features selected with both methods were classified with six different machine learning algorithms. In the study, the five-class public SIPaKMeD dataset was used. The proposed model achieved a high accuracy value of 99.02% on the relevant dataset. In order to compare the performances of the models used in the study, different metrics such as Accuracy, Recall, Precision and F1 Score were evaluated. The proposed model was also compared with the performances of 8 different Convolutional Neural Network's (CNN's) and 4 different ViT architectures accepted in the literature. The proposed model produced more successful results than traditional approaches and similar studies in the literature.Öğe A novel deep learning approach for predicting stone-free rates post-ESWL on uncontrasted CT(Peerj Inc, 2025) Efiloglu, Ozgur; Yildirim, Muhammed; Yildirim, Kadir; Bingol, Harun; Akalin, Mustafa Kaan; Culpan, Meftun; Yildirim, AsifExtracorporeal shock wave lithotripsy (ESWL) is one of the most often employed therapy methods for managing kidney stones. In our work, we sought to assess the efficacy of the artificial intelligence model developed using non-contrast computed tomography (CT) images in predicting stone-free rates for ESWL. The main difference between this study and other studies is that it proposes an artificial intelligence-based model that predicts the success of ESWL treatment using artificial intelligence methods. Data from 910 patients who underwent ESWL between January 2016 and June 2021 were analyzed retrospectively. Since the local binary pattern (LBP) and histogram of oriented gradients (HOG) feature extraction methods gave more successful results than other methods, a new feature map was obtained using the neighborhood component analysis (NCA) dimension reduction method after combining the features obtained using these methods. Then, the reduced feature map was classified into classifiers. In conclusion, we analyzed the effect of ESWL treatment using different artificial intelligence methods and found that the prediction accuracy was 94% on average. Results were obtained from seven different convolutional neural networks (CNNs) and two textural-based models in the study. Since textural-based models achieved the highest success among these models, these models were used as the base in the proposed model. The proposed model achieved better results than nine different models used in the study. When the results obtained from the proposed hybrid model for ESWL prediction are examined, this model will guide experts in the treatment of the disease.Öğe Automated Classification of Brain Tumor Disease with a Novel CNN Relief and SVM-Based Deep Hybrid Model(Int Information & Engineering Technology Assoc, 2023) Bayram, Hande Yuksel; Bingol, Harun; Alatas, BilalThe brain tumor is a very dangerous type of cancer that can be seen in people of almost any age and usually results in the patient's death. Early detection of these tumors, which have many varieties, is extremely important in terms of the patient's survival, affecting the planning of treatment, just as with other types of cancer. Early diagnosis of the disease is usually performed by means of imaging devices. It takes a lot of expertise to analyze the MRI images and diagnose the brain tumor. In this study, a hybrid deep model is recommended that can be used effectively in the classification of the brain tumor. The proposed hybrid model is a Convolutional Neural Network (CNN)-based method that automatically classifies Magnetic Resonance (MR) images of three different types of brain tumors, Glioma, Meningioma and Pituitary successfully. Our model is basically going through these stages. First of all, the features from the two models that show the highest performance from pre-trained deep models are combined. The most effective features of the specification map obtained in the next phase were selected using the Relief method. At the last stage, classification was carried out with Support Vector Machine (SVM), one of the most known machine learning techniques. As a result of the experiments, the hybrid deep model we proposed obtained 93.2% accuracy. It seems that proposed hybrid method has very competitive results and is thought to be efficiently used to classify the brain tumor.Öğe Automated Early Detection of Skin Cancer Using a CNN-ViT-Attention-Based Hybrid Model(Mdpi, 2026) Kanat, Zekiye; Onal, Merve Kesim; Bingol, Harun; Sener, Serpil; Avci, Engin; Yildirim, MuhammedBackground/Objectives: Skin cancer is a very serious disease. There is a risk that the cancer will spread to other parts of the body as the cancerous tissue deepens. For this reason, early diagnosis is important because it allows for early initiation of treatment. This study proposes a hybrid model for the early diagnosis of skin cancer. Methods: The proposed model was developed using Convolutional Neural Networks (CNNs), Vision Transformer (ViT) architectures, and the k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Neural Network Classifiers, Decision Tree (DT), and Logistic Regression (LR) classifiers. Furthermore, the proposed model was fine-tuned to improve its disease diagnosis. Two attention mechanisms, channel and spatial, were used together in the proposed model. The HAM10000 dataset was used during the experiments. Class weighting was performed to ensure class-based balance in the dataset. Results: The proposed model was also compared with the CNN and ViT architectures frequently used in the literature. Among these models, the highest accuracy value of 95.1% was obtained with the proposed model. Conclusions: It is considered that the proposed model can be used as a decision support system for dermatologists in the diagnosis of skin cancer.Öğe Automatic classification of kidney CT images with relief based novel hybrid deep model(Peerj Inc, 2023) Bingol, Harun; Yildirim, Muhammed; Yildirim, Kadir; Alatas, BilalOne of the most crucial organs in the human body is the kidney. Usually, the patient does not realize the serious problems that arise in the kidneys in the early stages of the disease. Many kidney diseases can be detected and diagnosed by specialists with the help of routine computer tomography (CT) images. Early detection of kidney diseases is extremely important for the success of the treatment of the disease and for the prevention of other serious diseases. In this study, CT images of kidneys containing stones, tumors, and cysts were classified using the proposed hybrid model. Results were also obtained using pre-trained models that had been acknowledged in the literature to evaluate the effectiveness of the suggested model. The proposed model consists of 29 layers. While classifying kidney CT images, feature maps were obtained from the convolution 6 and convolution 7 layers of the proposed model, and these feature maps were combined after optimizing with the Relief method. The wide neural network classifier then classifies the optimized feature map. While the highest accuracy value obtained in eight different pre-trained models was 87.75%, this accuracy value was 99.37% in the proposed model. In addition, different performance evaluation metrics were used to measure the performance of the model. These values show that the proposed model has reached high-performance values. Therefore, the proposed approach seems promising in order to automatically and effectively classify kidney CT images.Öğe Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model(Mdpi, 2023) Yildirim, Muhammed; Bingol, Harun; Cengil, Emine; Aslan, Serpil; Baykara, MuhammetUrine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.Öğe Automatic diagnosis of ureteral stone and degree of hydronephrosis with proposed convolutional neural network, RelieF, and gradient-weighted class activation mapping based deep hybrid model(Wiley, 2023) Bugday, Muhammet Serdar; Akcicek, Mehmet; Bingol, Harun; Yildirim, MuhammedUrinary system stone disease is a common disease group all over the world. Ureteral stones constitute 20% of all urinary system stones. Ureteral stones are important because they can cause hydronephrosis and related renal parenchymal damage in the kidneys. In the study, a hybrid model was developed to detect hydronephrosis and ureteral stones from kidney images. In the developed model, heat maps of the original images were obtained by using gradient-weighted class activation mapping (Grad-CAM) technology. Then, feature maps were extracted from both the original and heatmap datasets using the Efficientnetb0 architecture. Extracted feature maps were concatenated using a multimodal fusion technique. In this way, different features of an image are obtained. This has a positive effect on the performance of the model. The Relief dimension reduction technique was used to eliminate unnecessary features in the obtained feature map so that the proposed model can work faster and more effectively. Finally, the optimized feature map is classified in the support vector machine (SVM) classifier. To compare the performance of the proposed hybrid model, results were obtained with 8 state-of-the-art models accepted in the literature. Among these models, the highest accuracy value was achieved in the Efficientnetb0 architecture with 67.98%, whereas the accuracy of the proposed hybrid model was 91.1%. This value indicates that the proposed model can be used for HUN diagnosis.Öğe Automatic Diagnosis, Classification, and Segmentation of Abdominal Aortic Aneurysm and Dissection from Computed Tomography Images(Mdpi, 2025) Baltaci, Hakan; Yalcin, Sercan; Yildirim, Muhammed; Bingol, HarunBackground/Objectives: Diagnosis of abdominal aortic aneurysm and abdominal aortic dissection (AAA and AAD) is of strategic importance as cardiovascular disease has fatal implications worldwide. This study presents a novel deep learning-based approach for the accurate and efficient diagnosis of abdominal aortic aneurysms (AAAs) and aortic dissections (AADs) from CT images. Methods: Our proposed convolutional neural network (CNN) architecture effectively extracts relevant features from CT scans and classifies regions as normal or diseased. Additionally, the model accurately delineates the boundaries of detected aneurysms and dissections, aiding in clinical decision-making. A pyramid scene parsing network has been built in a hybrid method. The layer block after the classification layer is divided into two groups: whether there is an AAA or AAD region in the abdominal CT image, and determination of the borders of the detected diseased region in the medical image. Results: In this sense, both detection and segmentation are performed in AAA and AAD diseases. Python programming has been used to assess the accuracy and performance results of the proposed strategy. From the results, average accuracy rates of 83.48%, 86.9%, 88.25%, and 89.64% were achieved using ResDenseUNet, INet, C-Net, and the proposed strategy, respectively. Also, intersection over union (IoU) of 79.24%, 81.63%, 82.48%, and 83.76% have been achieved using ResDenseUNet, INet, C-Net, and the proposed method. Conclusions: The proposed strategy is a promising technique for automatically diagnosing AAA and AAD, thereby reducing the workload of cardiovascular surgeons.Öğe Chaos enhanced intelligent optimization-based novel deception detection system(Pergamon-Elsevier Science Ltd, 2023) Bingol, Harun; Alatas, BilalToday, with the developing technology, access methods to information have also changed. Internet blogs and news sites, social media, etc. replace the traditional information access tools such as TV, radio, newspaper, and magazines. Cheaper and faster access than traditional methods and easy access from anywhere where the internet is available are the main factors in changing the access method to information. Besides, information spreads rapidly on the internet without proving the accuracy. There may be many reasons for the distribution of misinformation in this way, such as commercial, political, and economic. Information with fake content and deception purposes negatively affects both the person and the society. It is extremely important to detect deceptive information in textual data and researchers continue to propose new models in order to obtain efficient results in terms of many metrics. This paper proposes a new approach for deception detection problems. The deception detection problem was considered an optimization problem for the first time. Optics Inspired Opti-mization (OIO), Grey Wolf Optimization (GWO), and Chaos Based Optics Inspired Optimization (CBOIOs) are adapted and modeled for the first time in the deception detection problem. Deception detection models proposed in this article include data preprocessing, adapting optimization methods, and testing stages. An experimental evaluation and comparison of the proposed model and seven supervised machine learning algorithms are per-formed within two different data sets depending on four evaluation metrics (Accuracy, Recall, Precision, and F -Measure). Results show that CBOIOs are more effective than OIO, GWO, and other machine learning algorithms for this problem.Öğe Classification of OME with Eardrum Otoendoscopic Images Using Hybrid-Based Deep Models, NCA, and Gaussian Method(Int Information & Engineering Technology Assoc, 2022) Bingol, HarunOtitis media with effusion (OME) is defined as a middle ear disease that occurs with the accumulation of fluid in the posterior part of the eardrum, usually without any symptoms. When OME disease is not treated, some negative consequences arise that deeply affect the education, social and cultural life of the patient. OME disease is a difficult issue to diagnose by specialists. In this article, autoendoscopic images of the eardrum have been classified using deep learning methods to help specialists in the diagnosis of OME. In this study, a hybrid deep model based on artificial intelligence is proposed. In the proposed hybrid model, feature maps were obtained using Efficientnetb0 and Densenet201 architectures from both the original dataset and the improved dataset using the gaussian method. Then, the merging process was applied to these feature maps. Unnecessary features are eliminated by applying NCA dimension reduction to the combined feature map. The most valuable features obtained at the end of the optimization process are classified in different machine learning classifiers. The proposed model reached a very competitive accuracy value of 98.20% in the SVM classifier.Öğe Convolutional Neural Networks Based Hybrid Deep Model for Grapevine Leaves Detection and Classification(Int Information & Engineering Technology Assoc, 2025) Atesoglu, Fatih; Bingol, HarunThe problem of determining the type of grapevine leave (GL) has an important place in the agricultural field and especially in the field of viticulture. It is a foodstuff that is consumed as a table especially in every Middle Eastern country and its export to Europe has been increasing in recent years. GL are usually consumed as wraps. Considering the economic situation of such a widely used plant, the determination of the plant type is very important. Because early intervention is required for diseases that will occur in the plant. As it is known, early diagnosis will facilitate treatment. In this study, leaf types were classified using artificial intelligence techniques in order to help experts diagnose the type of vine leaf. In addition, a hybrid model was proposed for classification processes. In this proposed hybrid model, feature maps were first extracted from the Resnet50 and Inceptionv3 deep models and these features were combined. AThen, the most valuable features in the feature map were selected with the Neighborhood Component Analysis (NCA) method. Then, the feature map containing the most valuable features was classified in the best-known supervised classification methods. The proposed hybrid model reached an accuracy value of 89.2%. Thus, it has been determined that the proposed hybrid model achieves a highly competitive accuracy value in the classification of vine leaf images and can be used for this purpose.Öğe 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, MuhammedBackgroundTreatment 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.Öğe 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, BilalOsteoarthritis (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.Öğe Hybrid Deep Model for Automated Detection of Tomato Leaf Diseases(Int Information & Engineering Technology Assoc, 2022) Bayram, Hande Yuksel; Bingol, Harun; Alatas, BilalTomatoes are preferred by farmers because of their high productivity. This fruit has a fibrous structure and contains plenty of vitamins. Tomato diseases are generally observed on stem, fruit, and leaves. Early diagnosis of the disease in plants is of vital importance for the plant. This is very important for farmers who expect economic gain from that plant. Because if the disease is not treated early, these tomatoes should be destroyed. For these reasons, systems to diagnose the disease early are very important. In this study, a tomato leaf diseases classification model developed with deep learning methods, which is one of the most popular artificial intelligence techniques, is proposed in order to eliminate the possibility of the human eye being mistaken. In this study, 6 different Convolutional Neural Network (CNN) architectures were used. In the first stage of this study, which consists of two stages, the classification process was carried out with the Alexnet, Googlenet, Shufflenet, Efficientb0, Resnet50, and Inceptionv3 architectures that were previously trained. In the second stage, feature maps of tomato leaf images in the dataset were obtained using the six pre-trained deep learning architectures. In the hybrid model proposed in this study, the feature maps extracted using the best two of the six deep learning models are concatenated. Then, the Neighborhood Component Analysis (NCA) method was applied to the extracted features in order to speed up the system, unnecessary features were removed and optimized. The optimized feature map is classified by traditional intelligent classification models. As a result of experimental studies, the average accuracy rate of the proposed model is 99.50 percent.Öğe Multi-feature fusion and dandelion optimizer based model for automatically diagnosing the gastrointestinal diseases(Peerj Inc, 2024) Kiziloluk, Soner; Yildirim, Muhammed; Bingol, Harun; Alatas, BilalIt is a known fact that gastrointestinal diseases are extremely common among the public. The most common of these diseases are gastritis, reflux, and dyspepsia. Since the symptoms of these diseases are similar, diagnosis can often be confused. Therefore, it is of great importance to make these diagnoses faster and more accurate by using computer-aided systems. Therefore, in this article, a new artificial intelligence-based hybrid method was developed to classify images with high accuracy of anatomical landmarks that cause gastrointestinal diseases, pathological findings and polyps removed during endoscopy, which usually cause cancer. In the proposed method, firstly trained InceptionV3 and MobileNetV2 architectures are used and feature extraction is performed with these two architectures. Then, the features obtained from InceptionV3 and MobileNetV2 architectures are merged. Thanks to this merging process, different features belonging to the same images were brought together. However, these features contain irrelevant and redundant features that may have a negative impact on classification performance. Therefore, Dandelion Optimizer (DO), one of the most recent metaheuristic optimization algorithms, was used as a feature selector to select the appropriate features to improve the classification performance and support vector machine (SVM) was used as a classifier. In the experimental study, the proposed method was also compared with different convolutional neural network (CNN) models and it was found that the proposed method achieved better results. The accuracy value obtained in the proposed model is 93.88%.Öğe The Detection and Classification of Grape Leaf Diseases with an Improved Hybrid Model Based on Feature Engineering and AI(Mdpi, 2025) Atesoglu, Fatih; Bingol, HarunThere are many products obtained from grapes. The early detection of diseases in an economically important fruit is important, and the spread of disease significantly increases financial losses. In recent years, it is known that artificial intelligence techniques have achieved very successful results in image classification. Therefore, the early detection and classification of grape diseases with the latest artificial intelligence techniques and feature reduction techniques was carried out within the scope of this study. The most well-known convolutional neural network (CNN) architectures, texture-based Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) methods, Neighborhood Component Analysis (NCA), feature reduction methods, and machine learning (ML) techniques are the methods used in this article. The proposed hybrid model was compared with two texture-based and four CNN models. The features from the most successful CNN model and texture-based architectures were combined. The NCA method was used to select the best features from the obtained feature map, and the model was classified using the best-known ML classifiers. Our proposed model achieved an accuracy value of 99.1%. This value shows that our model can be used in the detection of grape diseases.
