An important feature of image analysis is texture, seen in all images, from aerial and satellite images to microscopic images in biomedical research. A chest X-ray is the most common and effective method for diagnosing severe lung diseases such as cancer, pneumonia, and tuberculosis. The lungs are the largest X-ray object. The correct separation of the shapes and sizes of the contours of the lungs is an important reason for diagnosis, because of which an intelligent information environment can be created. Despite the use of X-rays, to identify the diagnosis, there is a chance that the disease will not be detected. In this sense, there is a risk of development, which may be fatal. The article deals with the problems of pneumonia clustering using the autocorrelation function to obtain the most accurate result. This provides a reliable tool for diagnosing lung radiographs. Image pre-processing and data shaping play an important role in revealing a well-functioning basis of the nervous system. Therefore, images from two classes were selected for the task: healthy and with pneumonia. This paper demonstrates the applicability of the autocorrelation function for highlighting interest in lung radiographs based on the fineness of textural features and k-means extraction.

The presented scientific work describes the results of the development and evaluation of two deep learning algorithms: long short-term memory with a convolutional neural network (LSTM+CNN) and long short-term memory with an autoencoder (LSTM+AE), designed for the diagnosis of autism spectrum disorders. The study focuses on the use of eye tracking technology to collect data on participants' eye movements while interacting with animated objects. These data were saved in NumPy array format (.npy) for ease of later analysis. The algorithms were evaluated in terms of their accuracy, generalization ability, and training time, which was confirmed by experts. The main goal of the study is to improve the diagnosis of autism, making it more accurate and effective. The convolutional neural network long short-term memory and autoencoder-long short-term memory models have shown promise as tools for achieving this goal, with the autoencoder model standing out for its ability to identify internal relationships in data. The article also discusses potential clinical applications of these algorithms and directions for future research.

This study proposes an interpretable hybrid deep learning framework for classifying attention deficit hyperactivity disorder (ADHD) using EEG signals recorded during cognitively demanding tasks. The core architecture integrates convolutional neural networks (CNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) layers to jointly capture spatial and temporal dynamics. In addition to the final hybrid architecture, the CNN–GRU–LSTM model alone demonstrates excellent accuracy (99.63%) with minimal variance, making it a strong baseline for clinical applications. To evaluate the role of global attention mechanisms, transformer encoder models with two and three attention blocks, along with a spatiotemporal transformer employing 2D positional encoding, are benchmarked. A hybrid CNN–RNN–transformer model is introduced, combining convolutional, recurrent, and transformer-based modules into a unified architecture. To enhance interpretability, SHapley Additive exPlanations (SHAP) are employed to identify key EEG channels contributing to classification outcomes. Experimental evaluation using stratified five-fold cross-validation demonstrates that the proposed hybrid model achieves superior performance, with average accuracy exceeding 99.98%, F1-scores above 0.9999, and near-perfect AUC and Matthews correlation coefficients. In contrast, transformer-only models, despite high training accuracy, exhibit reduced generalization. SHAP-based analysis confirms the hybrid model’s clinical relevance. This work advances the development of transparent and reliable EEG-based tools for pediatric ADHD screening.

Objective and reproducible diagnosis of attention-deficit/hyperactivity disorder (ADHD) remains challenging because of the limited availability of reliable electroencephalography (EEG) biomarkers and the high variability of neural signals. This study proposes a computational framework for ADHD detection based on dynamic functional connectivity and network-index modeling. Multichannel EEG recordings were transformed into temporal connectivity graphs using sliding-window correlations of band-limited amplitude envelopes. Several network-index models were evaluated, including linear, graph-based, recurrent, and hybrid spatio-temporal approaches. The proposed Hybrid Spatio-Temporal Graph Transformer demonstrated moderate, yet reproducible, subject-level classification performance. On the independent test set, the model achieved an accuracy of 63.16%, a balanced accuracy of 62.22%, a sensitivity of 80.00%, a specificity of 44.44%, an F1-score of 69.57%, and an AUC-ROC of 0.7444. Additional analysis of the derived network index demonstrated moderate intergroup separability, with a mean index shift of 1.16, Cohen’s d = 0.73, Pearson’s r = 0.36, and distribution overlap = 0.72. These findings suggest that the proposed framework captures informative spatio-temporal EEG connectivity patterns associated with ADHD; however, the model’s diagnostic applicability should be considered preliminary and requires validation in larger independent cohorts.

Early and objective diagnosis of autism spectrum disorders (ASD) remains a pressing issue in modern medical and cognitive informatics, where motor behavior analysis is considered a promising source of digital biomarkers. In particular, kinematic gait characteristics obtained with contactless sensors enable the identification of subtle motor impairments that may not be apparent on visual assessment. In this paper, we propose a hybrid multi-branch deep learning model, HMB-TSC, for the automatic classification of children with ASD based on spatiotemporal gait data extracted from the Kinect v2 camera. An open dataset containing gait recordings of children with ASD and typically developing (TD) peers was used for the experiments. Data preprocessing included outlier filtering, feature normalization, feature expansion, and time-series segmentation using a sliding-window method. The HMB-TSC architecture combines three complementary branches: a Transformer for modeling global temporal dependencies, a CNN-BiLSTM for extracting local spatiotemporal patterns, and a CNN-BiGRU for efficient sequential analysis. The performance evaluation was conducted using the holdout, 5-fold, and 5 × 3 nested validation protocols. In a reproducible released run, HMB-TSC achieved accuracy = 0.850 and AUROC = 0.948 in holdout validation, while 5-fold and nested validation yielded Accuracy = 0.850 ± 0.035, AUROC = 0.940 ± 0.029, and F1 = 0.845 ± 0.036. Compared with the baseline CNN, LSTM, and Transformer models, the proposed model demonstrated the best overall performance, especially in terms of AUROC and balanced F1-score.

The early and objective detection of neurodevelopmental conditions in children remains a critical challenge due to the reliance on subjective behavioral assessments and clinician-dependent interpretation. This study proposes a hybrid deep learning framework for the automated analysis of electroencephalographic (EEG) signals aimed at identifying neurodevelopmental patterns associated with attention and cognitive regulation disorders. The methodology integrates convolutional neural networks (CNN), gated recurrent units (GRU), and long short-term memory (LSTM) layers to extract both spatial and temporal EEG features. Preprocessing procedures include band-pass filtering, independent component analysis, segmentation into epochs, and z-score normalization. Experimental evaluation demonstrates high classification accuracy and robust generalization ability. Additionally, explainable artificial intelligence techniques are employed to interpret feature importance at the electrode level, increasing clinical transparency and trust. The obtained results confirm that hybrid deep learning architectures combined with interpretability tools can serve as reliable decision-support mechanisms in pediatric neurodiagnostics.