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Öğe An Accurate Multiple Sclerosis Detection Model Based on Exemplar Multiple Parameters Local Phase Quantization: ExMPLPQ(Multidisciplinary Digital Publishing Institute (MDPI), 2022) Macin, Gulay; Taşçı, Burak; Taşçı, İrem; Faust, Oliver; Barua, Prabal Datta; Doğan, Şengül; Tuncer, Türker; Tan, Ru-San; Acharya, U. RajendraMultiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients.Öğe An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images(Mdpi, 2023) Oztekin, Faruk; Katar, Oguzhan; Sadak, Ferhat; Yildirim, Muhammed; Cakar, Hakan; Aydogan, Murat; Acharya, U. RajendraDental caries is the most frequent dental health issue in the general population. Dental caries can result in extreme pain or infections, lowering people's quality of life. Applying machine learning models to automatically identify dental caries can lead to earlier treatment. However, physicians frequently find the model results unsatisfactory due to a lack of explainability. Our study attempts to address this issue with an explainable deep learning model for detecting dental caries. We tested three prominent pre-trained models, EfficientNet-B0, DenseNet-121, and ResNet-50, to determine which is best for the caries detection task. These models take panoramic images as the input, producing a caries-non-caries classification result and a heat map, which visualizes areas of interest on the tooth. The model performance was evaluated using whole panoramic images of 562 subjects. All three models produced remarkably similar results. However, the ResNet-50 model exhibited a slightly better performance when compared to EfficientNet-B0 and DenseNet-121. This model obtained an accuracy of 92.00%, a sensitivity of 87.33%, and an F1-score of 91.61%. Visual inspection showed us that the heat maps were also located in the areas with caries. The proposed explainable deep learning model diagnosed dental caries with high accuracy and reliability. The heat maps help to explain the classification results by indicating a region of suspected caries on the teeth. Dentists could use these heat maps to validate the classification results and reduce misclassification.Öğe Deep learning model for automated kidney stone detection using coronal CT images(Pergamon-Elsevier Science Ltd, 2021) Yildirim, Kadir; Bozdag, Pinar Gundogan; Talo, Muhammed; Yildirim, Ozal; Karabatak, Murat; Acharya, U. RajendraKidney stones are a common complaint worldwide, causing many people to admit to emergency rooms with severe pain. Various imaging techniques are used for the diagnosis of kidney stone disease. Specialists are needed for the interpretation and full diagnosis of these images. Computer-aided diagnosis systems are the practical approaches that can be used as auxiliary tools to assist the clinicians in their diagnosis. In this study, an automated detection of kidney stone (having stone/not) using coronal computed tomography (CT) images is proposed with deep learning (DL) technique which has recently made significant progress in the field of artificial intelligence. A total of 1799 images were used by taking different cross-sectional CT images for each person. Our developed automated model showed an accuracy of 96.82% using CT images in detecting the kidney stones. We have observed that our model is able to detect accurately the kidney stones of even small size. Our developed DL model yielded superior results with a larger dataset of 433 subjects and is ready for clinical application. This study shows that recently popular DL methods can be employed to address other challenging problems in urology.Öğe Deep learning-based PI-RADS score estimation to detect prostate cancer using multiparametric magnetic resonance imaging(Pergamon-Elsevier Science Ltd, 2022) Yildirim, Kadir; Yildirim, Muhammed; Eryesil, Hasan; Talo, Muhammed; Yildirim, Ozal; Karabatak, Murat; Acharya, U. RajendraProstate cancer (PCa) is the most common type of cancer among men. Digital rectal examination and prostate-specific antigen (PSA) tests are used to diagnose the PCa accurately. Since PSA is organ-specific and not disease-specific, multiparametric magnetic resonance imaging (mpMRI) is used to reduce unnecessary biopsies. Prostate imaging reporting and data system (PI-RADS) is widely used for mpMRI scoring to detect PCa. There is low-level agreement among interpreters and also subjectivity associated with PI-RADS scoring. Hence, in this study, a hybrid model has been proposed to accurately interpret mpMRI examination and predict PI-RADS scores. In the proposed systems, feature maps of mpMR images were extracted using the MobilenetV2, Efficientnetb0, and Darknet53 architectures. Then, the feature maps obtained using these three architectures were combined. The merged feature maps are subjected to neighborhood components analysis (NCA) to eliminate redundant features. The proposed system provided 96.09% accuracy.Öğe Early detection of colorectal cancer using a hybrid model with enhanced image quality and optimized classification(Springer, 2025) Bozdag, Ahmet; Karaduman, Mucahit; Kiziloluk, Soner; Karaduman, Gulsah; Yildirim, Muhammed; Yildirim, Ozal; Acharya, U. RajendraColorectal cancer starts in the large intestine and rectum. It develops when small, usually harmless growths called polyps become cancerous over time. Early diagnosis increases the chances of successfully treating colorectal cancer. A new hybrid model was developed to detect colorectal tissue types. In the first step of the model, the quality of the images was increased using Denoising Convolutional Neural Network (DNCNN) networks. The feature maps of the images were then obtained using DarkNet53 and shrunk using the Gorilla Troops Optimization Algorithm (GTO) to speed up the proposed model's performance and boost the performance. Finally, a support vector machine (SVM) classifier was used to classify the feature maps. The proposed model obtained an accuracy of 95.5% in classifying eight tissue types in colorectal cancer histopathology specimens (Adipose, Complex, Debris, Empty, Lympho, Mucosa, Stroma, and Tumor). To make the developed model more generalizable, robust, and accurate, it needs to be tested with a huge dataset collected from various centers and races.Öğe NFSDense201: microstructure image classification based on non-fixed size patch division with pre-trained DenseNet201 layers(Springer London Ltd, 2023) Barua, Prabal Datta; Dogan, Sengul; Kavuran, Gurkan; Tuncer, Turker; Tan, Ru-San; Acharya, U. RajendraIn the field of nanoscience, the scanning electron microscope (SEM) is widely employed to visualize the surface topography and composition of materials. In this study, we present a novel SEM image classification model called NFSDense201, which incorporates several key components. Firstly, we propose a unique nested patch division approach that divides each input image into four patches of varying dimensions. Secondly, we utilize DenseNet201, a deep neural network pretrained on ImageNet1k, to extract 2920 deep features from the last fully connected and global average pooling layers. Thirdly, we introduce an iterative neighborhood component analysis function to select the most discriminative features from the merged feature vector, which is formed by concatenating the four feature vectors extracted per input image. This process results in a final feature vector of optimal length 698. Lastly, we employ a standard shallow support vector machine classifier to perform the actual classification. To evaluate the performance of NFSDense201, we conducted experiments using a large public SEM image dataset. The dataset consists of 972, 162, 326, 4590, 3820, 3925, 4755, 181, 917, and 1624.jpeg images belonging to the following microstructural categories: biological, fibers, film-coated surfaces, MEMS devices and electrodes, nanowires, particles, pattern surfaces, porous sponge, powder, and tips, respectively. For both four-class and ten-class classification tasks, we evaluated NFSDense201 using subsets of the dataset containing 5080 and 21,272 images, respectively. The results demonstrate the superior performance of NFSDense201, achieving a four-class classification accuracy rate of 99.53% and a ten-class classification accuracy rate of 97.09%. These accuracy rates compare favorably against previously published SEM image classification models. Additionally, we report the performance of NFSDense201 for each class in the dataset.Öğe Novel multi-cube extreme learning machine with sparse attention and ridge regression technique for robust machine learning(Elsevier, 2026) Zirekgur, Merve; Karakaya, Baris; Sengur, Abdulkadir; Acharya, U. RajendraThis study introduces a learning framework designed to enhance the representational capacity and stability of single-layer feed-forward networks (SLFN) when modelling nonlinear and high-dimensional data. To this end, the proposed multi-cube unit with sparse attention and ridge regularisation (MCU-SAR) method integrates three complementary components: (i) a multi-cube unit (MCU) architecture that explicitly encodes higher-order feature interactions, (ii) a sparse attention mechanism that suppresses low-informative multiplicative terms, and (iii) a ridge-regularised extreme learning machine (ELM) output layer to improve generalisation. The proposed model is evaluated on 25 publicly available datasets, including 17 classification and 8 regression tasks, and benchmarked against 15 baseline methods comprising gradient-based optimisation techniques, support vector machines (SVM), and various ELM-based approaches. Performance comparisons are conducted using the Friedman test. MCU-SAR demonstrates consistently strong performance, ranking first on the majority of the 25 benchmark datasets and achieving competitive accuracy in classification as well as low error levels in regression tasks, with all results supported by statistically significant p-values. These results demonstrate that the proposed framework provides a scalable, generalisable, and computationally efficient solution for both classification and regression problems, offering robust performance on engineering-oriented real-world datasets.Öğe PFP-LHCINCA: Pyramidal Fixed-Size Patch-Based Feature Extraction and Chi-Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images(Hindawi Limited, 2022) Kaplan, Ela; Ekinci, Tekin; Kaplan, Selcuk; Barua, Prabal Datta; Dogan, Sengul; Tuncer, Turker; Acharya, U. RajendraObjectives. Fetal sex determination with ultrasound (US) examination is indicated in pregnancies at risk of X-linked genetic disorders or ambiguous genitalia. However, misdiagnoses often arise due to operator inexperience and technical difficulties while acquiring diagnostic images. We aimed to develop an efficient automated US-based fetal sex classification model that can facilitate efficient screening and reduce misclassification. Methods. We have developed a novel feature engineering model termed PFP-LHCINCA that employs pyramidal fixed-size patch generation with average pooling-based image decomposition, handcrafted feature extraction based on local phase quantization (LPQ), and histogram of oriented gradients (HOG) to extract directional and textural features and used Chi-square iterative neighborhood component analysis feature selection (CINCA), which iteratively selects the most informative feature vector for each image that minimizes calculated feature parameter-derived k-nearest neighbor-based misclassification rates. The model was trained and tested on a sizeable expert-labeled dataset comprising 339 males' and 332 females' fetal US images. One transverse fetal US image per subject zoomed to the genital area and standardized to 256 × 256 size was used for analysis. Fetal sex was annotated by experts on US images and confirmed postnatally. Results. Standard model performance metrics were compared using five shallow classifiers - k-nearest neighbor (kNN), decision tree, naïve Bayes, linear discriminant, and support vector machine (SVM) - with the hyperparameters tuned using a Bayesian optimizer. The PFP-LHCINCA model achieved a sex classification accuracy of ≥88% with all five classifiers and the best accuracy rates (>98%) with kNN and SVM classifiers. Conclusions. US-based fetal sex classification is feasible and accurate using the presented PFP-LHCINCA model. The salutary results support its clinical use for fetal US image screening for sex classification. The model architecture can be modified into deep learning models for training larger datasets. © 2022 Ela Kaplan et al.Öğe PrismPatNet: Novel prism pattern network for accurate fault classification using engine sound signals(Wiley, 2023) Sahin, Sakir Engin; Gulhan, Gokhan; Barua, Prabal Datta; Tuncer, Turker; Dogan, Sengul; Faust, Oliver; Acharya, U. RajendraEngines are prone to various types of faults, and it is crucial to detect and indeed classify them accurately. However, manual fault type detection is time-consuming and error-prone. Automated fault type detection promises to reduce inter- and intra-observer variability while ensuring time invariant attention during the observation duration. We have proposed an automated fault-type detection model based on sound signals to realize these advantageous properties. We have named the detection model prism pattern network (PrismPatNet) to reflect the fact that our design incorporates a novel feature extraction algorithm that was inspired by a 3D prism shape. Our prism pattern model achieves high accuracy with low-computational complexity. It consists of three main phases: (i) prism pattern inspired multilevel feature generation and maximum pooling operator, (ii) feature ranking and feature selection using neighbourhood component analysis (NCA), and (iii) support vector machine (SVM) based classification. The maximum pooling operator decomposes the sound signal into six levels. The proposed prism pattern algorithm extracts parameter values from both the signal itself and its decompositions. The generated parameter values are merged and fed to the NCA algorithm, which extracts 512 features from that input. The resulting feature vectors are passed on to the SVM classifier, which labels the input as belonging to 1 of 27 classes. We have validated our model with a newly collected dataset containing the sound of (1) a normal engine and (2) 26 different types of engine faults. Our model reached an accuracy of 99.19% and 98.75% using 80:20 hold-out validation and 10-fold cross-validation, respectively. Compared with previous studies, our model achieved the highest overall classification accuracy even though our model was tasked with identifying significantly more fault classes. This performance indicates that our PrismPatNet model is ready to be installed in real-world applications.
