Journal of Electronics, Electromedical Engineering, and Medical Informatics

Optimized Recurrent Neural Network Based on Improved Bacterial Colony Optimization for Predicting Osteoporosis Diseases

2026-02-08T06:16:52+07:00

Osteoporosis is a silent disease before significant fragility fractures despite its high prevalence, and its screening rate is low. In predictive healthcare analytics, the Elman recurrent neural network (ERNN) has been widely used as a learning technique. Traditional learning algorithms have some limitations, such as slow convergence rates and local minima that prevent gradient descent from finding the global minimum of the error function. The main goal is to precisely estimate each individual's risk of developing osteoporosis. These forecasts are essential for prompt diagnosis and treatment, which have a significant influence on patient outcomes. Hence, the present research focuses on making a more efficient prediction method based on an optimized Elman recurrent neural network (ERNN) for predicting osteoporosis diseases. An optimized ERNN method, IBCO-ERNN, improved bacterial colony optimization (IBCO) by optimizing the ERNN weights and biases. The IBCO approach uses an iterative local search (ILS) algorithm to enhance convergence rate and avoid the local optima problem of conventional BCO. Subsequently, the IBCO is used to optimize the ERNN's weights and biases, thereby improving convergence speed and detection rate. The effectiveness of IBCO-ERNN is evaluated using four different types of osteoporosis datasets: Femoral neck, Lumbar spine, Femoral and Spine, and BMD datasets. The proposed IBCO-ERNN produced higher accuracy at 95.61%, 96.26%, 97.26%, and 97.54 % for the Femoral neck, Lumbar spine, Femoral, and Spine datasets, respectively. The experimental findings demonstrated that, compared with other predictors, the proposed IBCO-ERNN achieved respectable accuracy and rapid convergence.

A Multimodal Explainable-AI Approach for Deep-Learning-based Epileptic Seizure Detection

2026-02-04T18:16:24+07:00

Epilepsy carries a high risk of sudden death and increased premature mortality, highlighting the importance of automatic seizure detection to support faster diagnosis and treatment. The opacity of existing deep learning models limits their real-world application in diagnosing epileptic seizures, underscoring the need for more transparent and explainable systems. Limited research studies are available on Explainable Artificial Intelligence (XAI)-based epileptic seizure detection, and these studies provide only a visual explanation for the model’s behaviour. Additionally, these studies lack validation of the XAI outputs using quantitative measures. Thus, this research aims to develop an explainable epileptic seizure detection model to address the limitations of existing black-box deep learning approaches. It proposes a novel Hybrid Transformer-DenseNet121-XAI (HTD-MXAI) integrated model for detecting epileptic seizures from EEG data. The proposed model leverages advanced deep learning architectures, namely the Transformer and DenseNet121, for automatic feature extraction, while simultaneously extracting handcrafted features from the time, frequency, and spatial domains. The XAI techniques, such as Attention Weights, Saliency Maps, and SHapley Additive eXplanations (SHAP), are integrated with the proposed model to provide multimodal explainability for the model’s decision-making process. The results demonstrate that the proposed model outperforms state-of-the-art models for seizure detection. It achieves an overall (aggregated across subjects) accuracy of 99.14%, Sensitivity of 98.49%, and Specificity of 99.68% when applied to the CHB-MIT dataset. The Faithfulness score of 40.94% and completeness score of 1.00 indicate that the explanations provided by the XAI method for the model’s prediction are highly reliable. In conclusion, the proposed model offers a promising solution to the constraints, including the interpretability of black box models, limited multimodal explainability, and the validation of XAI techniques in the context of epileptic seizure detection.

Hybrid Separable Conv-ViT–CheXNet with Explainable Localization for Pneumonia Diagnosis

2026-02-21T10:10:10+07:00

This research presents a robust, interpretable, and computationally efficient deep learning framework for multiclass pneumonia classification from chest X-ray images, with a strong emphasis on diagnostic accuracy, model transparency, and real-time applicability in clinical settings. We propose SCViT-CheXNet, a novel hybrid architecture that integrates a Separable Convolution Vision Transformer (SCViT) with a simplified CheXNet backbone based on DenseNet121 to achieve efficient spatial feature extraction, hierarchical representation learning, and faster model convergence. The use of separable convolution significantly reduces computational complexity while preserving discriminative feature learning, and the transformer module effectively captures long-range dependencies in radiographic patterns. To address the critical issue of class imbalance inherent in medical imaging datasets, an Auxiliary Classifier Deep Convolutional Generative Adversarial Network (ADCGAN) is employed to generate synthetic samples for underrepresented pneumonia categories, thereby enhancing data diversity and improving model generalization. The proposed framework is extensively evaluated on two benchmark datasets: Dataset-1, consisting of Normal, Viral, Bacterial, and Fungal Pneumonia cases, and Dataset-2, comprising Normal, Viral Pneumonia, COVID-19, and Lung Opacity classes. Model interpretability is ensured through Gradient-weighted Class Activation Mapping (Grad-CAM), which enables visualization of disease-specific regions in chest X-ray images and validates the clinical relevance of the learned representations. Experimental results demonstrate that SCViT-CheXNet consistently outperforms existing convolutional neural network and transformer-based approaches, achieving 99% accuracy, precision, recall, and F1-score across both datasets. The synergistic integration of separable convolution, transformer-based feature modeling, and GAN-driven data augmentation results in a lightweight yet highly accurate and interpretable diagnostic system. Overall, the SCViT-CheXNet framework shows strong potential for deployment in automated pneumonia and COVID-19 screening systems, offering reliable support for real-time clinical decision-making and contributing to improved patient outcomes.