A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals.

Neuropsychiatric disorders are one of the leading causes of disability. Mental health problems can occur due to various biological and environmental factors. The absence of definitive confirmatory diagnostic tests for psychiatric disorders complicates the diagnosis. It's critical to distinguish between bipolar disorder, depression, and schizophrenia since their symptoms and treatments differ. Because of brain-heart autonomic connections, electrocardiography (ECG) signals can be changed in behavioral disorders. In this research, we have automatically classified bipolar, depression, and schizophrenia from ECG signals. In this work, a new hand-crafted feature engineering model has been proposed to detect psychiatric disorders automatically. The main objective of this model is to accurately detect psychiatric disorders using ECG beats with linear time complexity. Therefore, we collected a new ECG signal dataset containing 3,570 ECG beats with four categories. The used categories are bipolar, depression, schizophrenia, and control. Furthermore, a new ternary pattern-based signal classification model has been proposed to classify these four categories. Our proposal contains four essential phases, and these phases are (i) multileveled feature extraction using multilevel discrete wavelet transform and ternary pattern, (ii) the best features selection applying iterative Chi2 selector, (iii) classification with artificial neural network (ANN) to calculate lead wise results and (iv) calculation the voted/general classification accuracy using iterative majority voting (IMV) algorithm. tenfold cross-validation is one of the most used validation techniques in the literature, and this validation model gives robust classification results. Using ANN with tenfold cross-validation, lead-by-lead and voted results have been calculated. The lead-by-lead accuracy range of the proposed model using the ANN classifier is from 73.67 to 89.19%. By deploying the IMV method, the general classification performance of our ternary pattern-based ECG classification model is increased from 89.19 to 96.25%. The findings and the calculated classification accuracies (single lead and voted) clearly demonstrated the success of the proposed ternary pattern-based advanced signal processing model. By using this model, a new wearable device can be proposed.

A Pyramid Deep Feature Extraction Model for the Automatic Classification of Upper Extremity Fractures.

The musculoskeletal system plays a crucial role in our daily lives, and the accurate diagnosis of musculoskeletal issues is essential for providing effective healthcare. However, the classification of musculoskeletal system radiographs is a complex task, requiring both accuracy and efficiency. This study addresses this challenge by introducing and evaluating a pyramid deep feature extraction model for the automatic classification of musculoskeletal system radiographs. The primary goal of this research is to develop a reliable and efficient solution to classify different upper extremity regions in musculoskeletal radiographs. To achieve this goal, we conducted an end-to-end training process using a pre-trained EfficientNet B0 convolutional neural network (CNN) model. This model was trained on a dataset of radiographic images that were divided into patches of various sizes, including 224 × 224, 112 × 112, 56 × 56, and 28 × 28. From the trained CNN model, we extracted a total of 85,000 features. These features were subsequently subjected to selection using the neighborhood component analysis (NCA) feature selection algorithm and then classified using a support vector machine (SVM). The results of our experiments are highly promising. The proposed model successfully classified various upper extremity regions with high accuracy rates: 92.04% for the elbow region, 91.19% for the finger region, 92.11% for the forearm region, 91.34% for the hand region, 91.35% for the humerus region, 89.49% for the shoulder region, and 92.63% for the wrist region. These results demonstrate the effectiveness of our deep feature extraction model as a potential auxiliary tool in the automatic analysis of musculoskeletal system radiographs. By automating the classification of musculoskeletal radiographs, our model has the potential to significantly accelerate clinical diagnostic processes and provide more precise results. This advancement in medical imaging technology can ultimately lead to better healthcare services for patients. However, future studies are crucial to further refine and test the model for practical clinical applications, ensuring that it integrates seamlessly into medical diagnosis and treatment processes, thus improving the overall quality of healthcare services.

Attention TurkerNeXt: Investigations into Bipolar Disorder Detection Using OCT Images.

Background and Aim: In the era of deep learning, numerous models have emerged in the literature and various application domains. Transformer architectures, particularly, have gained popularity in deep learning, with diverse transformer-based computer vision algorithms. Attention convolutional neural networks (CNNs) have been introduced to enhance image classification capabilities. In this context, we propose a novel attention convolutional model with the primary objective of detecting bipolar disorder using optical coherence tomography (OCT) images. Materials and Methods: To facilitate our study, we curated a unique OCT image dataset, initially comprising two distinct cases. For the development of an automated OCT image detection system, we introduce a new attention convolutional neural network named "TurkerNeXt". This proposed Attention TurkerNeXt encompasses four key modules: (i) the patchify stem block, (ii) the Attention TurkerNeXt block, (iii) the patchify downsampling block, and (iv) the output block. In line with the swin transformer, we employed a patchify operation in this study. The design of the attention block, Attention TurkerNeXt, draws inspiration from ConvNeXt, with an added shortcut operation to mitigate the vanishing gradient problem. The overall architecture is influenced by ResNet18. Results: The dataset comprises two distinctive cases: (i) top to bottom and (ii) left to right. Each case contains 987 training and 328 test images. Our newly proposed Attention TurkerNeXt achieved 100% test and validation accuracies for both cases. Conclusions: We curated a novel OCT dataset and introduced a new CNN, named TurkerNeXt in this research. Based on the research findings and classification results, our proposed TurkerNeXt model demonstrated excellent classification performance. This investigation distinctly underscores the potential of OCT images as a biomarker for bipolar disorder.

Exemplar MobileNetV2-Based Artificial Intelligence for Robust and Accurate Diagnosis of Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that prominently affects young adults due to its debilitating nature. The pathogenesis of the disease is focused on the inflammation and neurodegeneration processes. Inflammation is associated with relapses, while neurodegeneration emerges in the progressive stages of the disease. Magnetic resonance imaging (MRI) is commonly used for the diagnosis of MS, and guidelines such as the McDonald criteria are available. MRI is an essential tool to demonstrate the spatial distribution and changes over time in the disease. This study discusses the use of image processing techniques for the diagnosis of MS and specifically combines the MobileNetV2 network with exemplar-based learning, IMrMr feature selection, and K-Nearest Neighbors (KNN) classification methods. Experiments conducted on two different datasets (Dataset 1 and Dataset 2) demonstrate that these methods provide high accuracy in diagnosing MS. Dataset 1 comprises 128 patients with 706 MRI images, 131 MS patients with 667 MRI images, and 150 healthy control subjects with 1373 MRI images. Dataset 2 includes an MS group with 650 MRI images and a healthy control group with 676 MRI images. The results of the study include 10-fold cross-validation results performed on different image sections (Axial, Sagittal, and Hybrid) for Dataset 1. Accuracy rates of 99.76% for Axial, 99.48% for Sagittal, and 98.02% for Hybrid sections were achieved. Furthermore, 100% accuracy was achieved on Dataset 2. In conclusion, this study demonstrates the effective use of powerful image processing methods such as the MobileNetV2 network and exemplar-based learning for the diagnosis of MS. These findings suggest that these methods can be further developed in future research and offer significant potential for clinical applications in the diagnosis and monitoring of MS.

ASNET: A Novel AI Framework for Accurate Ankylosing Spondylitis Diagnosis from MRI.

BACKGROUND: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients' quality of life. This study aims to diagnose AS with a pre-trained hybrid model using magnetic resonance imaging (MRI). MATERIALS AND METHODS: In this research, we collected a new MRI dataset comprising three cases. Furthermore, we introduced a novel deep feature engineering model. Within this model, we utilized three renowned pretrained convolutional neural networks (CNNs): DenseNet201, ResNet50, and ShuffleNet. Through these pretrained CNNs, deep features were generated using the transfer learning approach. For each pretrained network, two feature vectors were generated from an MRI. Three feature selectors were employed during the feature selection phase, amplifying the number of features from 6 to 18 (calculated as 6 × 3). The k-nearest neighbors (kNN) classifier was utilized in the classification phase to determine classification results. During the information phase, the iterative majority voting (IMV) algorithm was applied to secure voted results, and our model selected the output with the highest classification accuracy. In this manner, we have introduced a self-organized deep feature engineering model. RESULTS: We have applied the presented model to the collected dataset. The proposed method yielded 99.80%, 99.60%, 100%, and 99.80% results for accuracy, recall, precision, and F1-score for the collected axial images dataset. The collected coronal image dataset yielded 99.45%, 99.20%, 99.70%, and 99.45% results for accuracy, recall, precision, and F1-score, respectively. As for contrast-enhanced images, accuracy of 95.62%, recall of 80.72%, precision of 94.24%, and an F1-score of 86.96% were attained. CONCLUSIONS: Based on the results, the proposed method for classifying AS disease has demonstrated successful outcomes using MRI. The model has been tested on three cases, and its consistently high classification performance across all cases underscores the model's general robustness. Furthermore, the ability to diagnose AS disease using only axial images, without the need for contrast-enhanced MRI, represents a significant advancement in both healthcare and economic terms.

Primate brain pattern-based automated Alzheimer's disease detection model using EEG signals.

Electroencephalography (EEG) may detect early changes in Alzheimer's disease (AD), a debilitating progressive neurodegenerative disease. We have developed an automated AD detection model using a novel directed graph for local texture feature extraction with EEG signals. The proposed graph was created from a topological map of the macroscopic connectome, i.e., neuronal pathways linking anatomo-functional brain segments involved in visual object recognition and motor response in the primate brain. This primate brain pattern (PBP)-based model was tested on a public AD EEG signal dataset. The dataset comprised 16-channel EEG signal recordings of 12 AD patients and 11 healthy controls. While PBP could generate 448 low-level features per one-dimensional EEG signal, combining it with tunable q-factor wavelet transform created a multilevel feature extractor (which mimicked deep models) to generate 8,512 (= 448 × 19) features per signal input. Iterative neighborhood component analysis was used to choose the most discriminative features (the number of optimal features varied among the individual EEG channels) to feed to a weighted k-nearest neighbor (KNN) classifier for binary classification into AD vs. healthy using both leave-one subject-out (LOSO) and tenfold cross-validations. Iterative majority voting was used to compute subject-level general performance results from the individual channel classification outputs. Channel-wise, as well as subject-level general results demonstrated exemplary performance. In addition, the model attained 100% and 92.01% accuracy for AD vs. healthy classification using the KNN classifier with tenfold and LOSO cross-validations, respectively. Our developed multilevel PBP-based model extracted discriminative features from EEG signals and paved the way for further development of models inspired by the brain connectome.

Attention Deep Feature Extraction from Brain MRIs in Explainable Mode: DGXAINet.

Artificial intelligence models do not provide information about exactly how the predictions are reached. This lack of transparency is a major drawback. Particularly in medical applications, interest in explainable artificial intelligence (XAI), which helps to develop methods of visualizing, explaining, and analyzing deep learning models, has increased recently. With explainable artificial intelligence, it is possible to understand whether the solutions offered by deep learning techniques are safe. This paper aims to diagnose a fatal disease such as a brain tumor faster and more accurately using XAI methods. In this study, we preferred datasets that are widely used in the literature, such as the four-class kaggle brain tumor dataset (Dataset I) and the three-class figshare brain tumor dataset (Dataset II). To extract features, a pre-trained deep learning model is chosen. DenseNet201 is used as the feature extractor in this case. The proposed automated brain tumor detection model includes five stages. First, training of brain MR images with DenseNet201, the tumor area was segmented with GradCAM. The features were extracted from DenseNet201 trained using the exemplar method. Extracted features were selected with iterative neighborhood component (INCA) feature selector. Finally, the selected features were classified using support vector machine (SVM) with 10-fold cross-validation. An accuracy of 98.65% and 99.97%, were obtained for Datasets I and II, respectively. The proposed model obtained higher performance than the state-of-the-art methods and can be used to aid radiologists in their diagnosis.

An Effective and Robust Approach Based on R-CNN+LSTM Model and NCAR Feature Selection for Ophthalmological Disease Detection from Fundus Images.

Changes in and around anatomical structures such as blood vessels, optic disc, fovea, and macula can lead to ophthalmological diseases such as diabetic retinopathy, glaucoma, age-related macular degeneration (AMD), myopia, hypertension, and cataracts. If these diseases are not diagnosed early, they may cause partial or complete loss of vision in patients. Fundus imaging is the primary method used to diagnose ophthalmologic diseases. In this study, a powerful R-CNN+LSTM-based approach is proposed that automatically detects eight different ophthalmologic diseases from fundus images. Deep features were extracted from fundus images with the proposed R-CNN+LSTM structure. Among the deep features extracted, those with high representative power were selected with an approach called NCAR, which is a multilevel feature selection algorithm. In the classification phase, the SVM algorithm, which is a powerful classifier, was used. The proposed approach is evaluated on the eight-class ODIR dataset. The accuracy (main metric), sensitivity, specificity, and precision metrics were used for the performance evaluation of the proposed approach. Besides, the performance of the proposed approach was compared with the existing approaches using the ODIR dataset.