Journal of Electronics, Electromedical Engineering, and Medical Informatics

Rule-Based Adaptive Chatbot on WhatsApp for Visual, Auditory, and Kinesthetic Learning Style Detection

Muhammad Rahulil — 2025-11-28

Adapting learning methods to individual learning styles remains a major challenge in digital education due to the static nature of traditional questionnaires and the absence of adaptive feedback mechanisms. This study aimed to develop a rule-based adaptive WhatsApp chatbot capable of automatically identifying users’ learning styles, visual, auditory, and kinesthetic, through a weighted questionnaire enhanced with probabilistic refinement. The proposed system introduces an adaptive decision framework that dynamically manages conversation flow using score dominance evaluation, early termination, and selective question expansion. Bayesian posterior probability estimation is employed to strengthen decision confidence in borderline cases, ensuring consistent and interpretable results even when user responses are ambiguous. The chatbot was implemented using WhatsApp-web.js and MongoDB, supported by session validation and activity log monitoring to ensure operational reliability and data integrity. System validation involved white-box testing using Cyclomatic Complexity to verify logical accuracy and 20-fold cross-validation using a Support Vector Machine (SVM) to evaluate classification performance. The adaptive model achieved an accuracy of 80.2% and an AUC of 0.902, supported by a balanced precision (0.738), recall (0.662), and F1-score (0.698). These results demonstrate stable discriminative capability and confirm that the adaptive scoring mechanism effectively reduces redundant questioning, lowers cognitive load, and improves interaction efficiency without compromising reliability. In conclusion, the study successfully achieved its objective of developing an adaptive, efficient, and mathematically transparent learning style detection system. The findings confirm that adaptive rule-based logic reinforced by probabilistic reasoning can significantly enhance the efficiency and reliability of digital learning assessments. Future research will extend this framework by incorporating multimodal behavioral indicators and personalized learning content to further strengthen adaptive learning support

BTISS-WNET: Deep Learning-based Brain Tissue Segmentation using Spatio Temporal WNET

Athur Shaik Ali Gousia Banu — 2025-11-26

Brain tissue segmentation (BTISS) from magnetic resonance imaging (MRI) is a critical process in neuroimaging, aiding in the analysis of brain morphology and facilitating accurate diagnosis and treatment of neurological disorders. A major challenge in BTISS is intensity inhomogeneity, which arises from variations in the magnetic field during image acquisition. This results in non-uniform intensities within the same tissue class, particularly affecting white matter (WM) segmentation. To address this problem, we propose an efficient deep learning-based framework, BTISS-WNET, for accurate segmentation of brain tissues. The main contribution of this work is the integration of a spatio-temporal segmentation strategy with advanced pre-processing and feature extraction to overcome intensity inconsistency and improve tissue differentiation. The process begins with skull stripping to eliminate non-brain tissues, followed by Empirical Wavelet Transform (EWT) for noise reduction and edge enhancement. Data augmentation techniques, including random rotation and flipping, are applied to improve model generalization. The preprocessed images are fed into Res-GoogleNet (RGNet) to extract deep semantic features. Finally, a Spatio-Temporal WNet is used for precise WM segmentation, leveraging spatial and temporal dependencies for improved boundary delineation. The proposed BTISS-WNET model achieves a segmentation accuracy of 99.32% for white matter. It also demonstrates improved accuracy of 1.76%, 18.23%, and 16.02% over DDSeg, BISON, and HMRF-WOA, respectively. In conclusion, BTISS-WNET provides a robust and high-accuracy framework for WM segmentation in MRI images, with promising applications in clinical neuroimaging. Future work will focus on validating the model using real clinical datasets and extending it to multi-tissue and multi-modal MRI segmentation

Deep Learning Based Ovarian Cancer Classification Using EfficientNetB2 with Attention Mechanism

Jayashri Kolekar — 2025-11-28

Ovarian cancer is a gynecological malignancy comprising multiple histopathological subtypes. Traditional diagnostic tools like histopathology and CA-125 tests suffer from limitations, including inter-observer variability, low specificity, and time-consuming procedures, often leading to delayed or incorrect diagnoses, which are subjective and error-prone. Conventional machine learning models, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), have been applied but often struggle with high-dimensional image data and fail to extract deep morphological features. This study proposes a DL-based framework to classify ovarian cancer subtypes from histopathological images, aiming to enhance diagnostic accuracy and clinical decision-making. Initially, Deep learning was applied using pre-trained architectures such as VGG-16, Xception, and EfficientNetB2. However, the standout innovation in this study is the integration of EfficientNetB2 with Convolutional Block Attention Module (CBAM), an attention mechanism module. An attention mechanism allows the model to focus on the most informative regions of the image, thereby improving diagnostic precision. The proposed system was trained and validated on a diverse, well-structured dataset, achieving high accuracy and strong generalization capability. EfficientNetB2 with CBAM outperformed other models, achieving a 91% accuracy rate compared to 52% for VGG-16, 72% for Xception, and 82% for the baseline EfficientNetB2 model. This attention-enhanced, scalable AI model demonstrates strong potential for clinical application. It provides faster and more efficient classification of ovarian cancer subtypes compared to conventional approaches. The framework has the potential to improve survival outcomes for patients with ovarian cancer. The proposed system demonstrates a significant improvement in ovarian cancer subtype classification (High-Grade Serous Carcinoma, Low-Grade Serous Carcinoma, Clear-Cell, Endometrioid, and Mucinous Carcinoma). It provides a practical tool for aiding early diagnosis and treatment planning, with potential for integration into clinical workflows.

DCRNet: Hybrid Deep Learning Architecture for Forecasting of Blood Glucose

Ketan Lad — 2025-12-07

Maintaining blood glucose (BG) levels within the euglycemic range is essential for patients with type 1 diabetes (T1D) to prevent both hypoglycemia and hyperglycemia. Often, BG concentration changes due to unannounced carbohydrate intake during meals or an inappropriate amount of insulin dosage. Timely forecasting of BG can help take appropriate actions in advance to keep BG within the euglycemic range. Recent studies indicate that deep learning techniques have demonstrated improved performance in this field. Deep learning approaches often struggle to precisely predict future BG levels. To address these challenges, this paper introduces a novel hybrid deep learning architecture called DCRNet. This architecture incorporates a Dilated Convolution layer that effectively detects multi-scale patterns while minimizing parameter count. Additionally, it utilizes Long Short-Term Memory (LSTM) to handle contextual dependencies and maintain the temporal order of the extracted features. DCRNet predicts future BG levels for short-term durations (15, 30, and 60 minutes) using information on glucose, meals, and insulin dosages. The proposed architecture’s performance is evaluated on 11 simulated subjects from the UVA/Padova T1D Mellitus simulator and 12 actual subjects from the OhioT1DM dataset. In contrast to previous works, the proposed architecture achieves root mean square errors (RMSEs) of 3.42, 6.45, and 17.73 mg/dL for simulated subjects and 12.57, 20.72, and 34.41mg/dL for actual subjects, for prediction horizons (PH) of 15-, 30-, and 60-minute, respectively. The proposed architecture is also evaluated using the mean absolute error (MAE), which is 2.11, 4.47, and 11.78 mg/dL for simulated subjects and 7.9, 14.13, and 25.5 mg/dL for actual subjects, for 15-, 30-, and 60-minute PH. The experimental findings validate that the proposed architecture, which uses a dilated convolutional LSTM, outperforms other recent state-of-the-art models.

A Neuro-Physiological Diffusion Model for Accurate EEG-Based Psychiatric Disorder Classification

Pradeep Gopal — 2025-12-07

Identification of psychiatric conditions such as depression, schizophrenia, anxiety, and obsessive-compulsive disorder (OCD) from Electroencephalography (EEG) data remains a significant challenge due to the complexity of neurophysiological patterns. While Generative Adversarial Networks (GANs) have been explored to augment EEG datasets and enhance classifier performance, they often suffer from limitations including training instability, mode collapse, and the generation of physiologically implausible EEG samples. These shortcomings hinder their applicability in high-stakes clinical decision-making, where reliability and physiological coherence are critical. This study aims to address the above-mentioned challenges by proposing a novel Neuro-Physiologically Constrained Diffusion Framework (NPC-DiffEEG). This framework leverages the strengths of conditional diffusion models while integrating domain-specific neurophysiological constraints, ensuring that generated EEG signals preserve key properties, such as frequency band structures and inter-channel connectivity patterns, both of which are essential for accurate mental disorder classification. The NPC-DiffEEG-generated data is combined with real EEG features and processed using a multi-task attention-based transformer, enabling the model to learn robust, cross-disorder representations. Extensive experiments conducted on a publicly available multi-disorder EEG dataset demonstrate that NPC-DiffEEG significantly outperforms traditional GAN-based augmentation approaches. The model achieves an impressive average classification accuracy of 96.8%, along with superior F1-scores and AUC values across all disorder categories. Furthermore, integrating attention-based disorder attribution not only enhances interpretability but also reduces overfitting, thereby improving generalizability to unseen subjects. This innovative approach marks a substantial advancement in EEG-based classification of psychiatric disorders, bridging the gap between synthetic data generation and clinically reliable decision-support systems.

Comparative Analysis of YOLO11 and Mask R-CNN for Automated Glaucoma Detection

Muhammad Naufaldi Fayyadh — 2025-12-08

Glaucoma is a progressive optic neuropathy and a major cause of irreversible blindness. Early detection is crucial, yet current practice depends on manual estimation of the vertical Cup-to-Disc Ratio (vCDR), which is subjective and inefficient. Automated fundus image analysis provides scalable solutions but is challenged by low optic cup contrast, dataset variability, and the need for clinically interpretable outcomes. This study aimed to develop and evaluate an automated glaucoma screening pipeline based on optic disc (OD) and optic cup (OC) segmentation, comparing a single-stage model (YOLO11-Segmentation) with a two-stage model (Mask R-CNN with ResNet50-FPN), and validating it using vCDR at a threshold of 0.7. The contributions are fourfold: establishing a benchmark comparison of YOLO11 and Mask R-CNN across three datasets (REFUGE, ORIGA, G1020); linking segmentation accuracy to vCDR-based screening; analyzing precision–recall trade-offs between the models; and providing a reproducible baseline for future studies. The pipeline employed standardized preprocessing (optic nerve head cropping, resizing to 1024×1024, conservative augmentation). YOLO11 was trained for 200 epochs, and Mask R-CNN for 75 epochs. Evaluation metrics included Dice, Intersection over Union (IoU), mean absolute error (MAE), correlation, and classification performance. Results showed that Mask R-CNN achieved higher disc Dice (0.947 in G1020, 0.938 in REFUGE) and recall (0.880 in REFUGE), while YOLO11 attained stronger vCDR correlation (r = 0.900 in ORIGA) and perfect precision (1.000 in G1020). Overall accuracy exceeded 0.92 in REFUGE and G1020. In conclusion, YOLO11 favored conservative screening with fewer false positives, while Mask R-CNN improved sensitivity. These complementary strengths highlight the importance of model selection by screening context and suggest future research on hybrid frameworks and multimodal integration

Graph-Theoretic Analysis of Electroencephalography Functional Connectivity Using Phase Lag Index for Detection of Ictal States

Ghansyamkumar Rathod — 2025-12-09

Epileptic disorders are characterized by the misfiring of neurons and affect 50 million people worldwide, who have to live with physical challenges in their normal lives. The ionic activity of the brain can be detected as an electrical activity from the scalp using a non-invasive bio-potential measurement technique known as electroencephalography (EEG). Manual interpretation of brainwaves is a time-consuming, expert-intensive task. In recent years, AI has achieved remarkable results, but at the cost of large datasets and high processing power. We used publicly available online datasets from the Children’s Hospital Boston (CHB) in collaboration with the Massachusetts Institute of Technology (MIT). The datasets consisted of 23 bipolar channels that included pre-processed epochs of both normal and pre-labeled seizure (ictal) states. Using the Phase Lag Index (PLI), the functional connectivity of the network was built to record consistent phase synchronization while minimizing artifacts from volume conduction. Graph-theory-based features were used to detect the brain's seizure state. A significant increase in the values of graph theoretical features, such as degree centrality and clustering coefficient, was observed, along with the formation of hyper-connected hubs and disrupted brain communication in the ictal state. Statistical tests (T-tests, ANOVA, Mann-Whitney U) across multiple PLI thresholds confirmed consistent significant differences (p-value < 0.05) between normal and ictal conditions. This study aims to provide a method based on graph theory, which is computationally efficient, interpretable, and suitable for real-time seizure detection. Considering the efficiency of clustering coefficient and degree of centrality, we can say that they are useful biomarkers for biomedical applications.

Enhancing Deep Learning Model Using Whale Optimization Algorithm on Brain Tumor MRI

Winarno Winarno — 2025-12-18

The increasing prevalence of brain cancer has emerged as a significant global health issue, with brain neoplasms, particularly gliomas, presenting considerable diagnostic and therapeutic obstacles. The timely and precise identification of such tumors is crucial for improving patient outcomes. This investigation explores the advancement of Convolutional Neural Networks (CNNs) for detecting brain tumors using MRI data, incorporating the Whale Optimization Algorithm (WOA) for the automated tuning of hyperparameters. Moreover, two callbacks, ReduceLROnPlateau and early stopping, were utilized to augment training efficacy and model resilience. The proposed model exhibited exceptional performance across all tumor categories. Specifically, the precision, recall, and F1-scores for Glioma were recorded as 0.997, 0.980, and 0.988, respectively; for meningioma, as 0.983, 0.986, and 0.984; for no tumors, as 0.998, 0.998, and 0.998; and for pituitary, as 0.997, 0.997, and 0.997. The mean performance metrics attained were 0.994 for precision, 0.990 for recall, and 0.992 for F1-score. The overall accuracy of the model was determined to be 0.991. Notably, incorporating callbacks within the CNN architecture improved accuracy to 0.994. Furthermore, when synergized with the WOA, the CNN-WOA model achieved a maximum accuracy of 0.996. This advancement highlights the effectiveness of integrating adaptive learning methodologies with metaheuristic optimization techniques. The findings suggest that the model sustains high classification accuracy across diverse tumor types and exhibits stability and robustness throughout training. The amalgamation of callbacks and the Whale Optimization Algorithm significantly bolster CNN performance in classifying brain tumors. These advancements contribute to the development of more reliable diagnostic instruments in medical imaging

EPR-Stego: Quality-Preserving Steganographic Framework for Securing Electronic Patient Records

Wardatul Amalia Safitri — 2025-12-18

Secure medical data transmission is a fundamental requirement in telemedicine, where information is often exchanged over public networks. Protecting patient confidentiality and ensuring data integrity are crucial, particularly when sensitive medical records are involved. Steganography, an information hiding technique, offers a promising solution by embedding confidential data within medical images. This approach not only safeguards privacy but also supports authentication processes, ensuring that patient information remains secure during transmission. This study introduces EPR-Stego, a novel steganographic framework designed specifically for embedding electronic patient record (EPR) data in medical images. The key innovation of EPR-Stego lies in its mathematical strategy to minimize pixel intensity differences between neighboring pixels. By reducing usable pixel variations, the framework generates a stego image that is visually indistinguishable from the original, thereby enhancing imperceptibility while preserving diagnostic quality. Additionally, the method produces a key table, required by the recipient to accurately extract the embedded data, which further strengthens security against unauthorized access. The design of EPR-Stego aims to prevent attackers from easily detecting the presence of hidden medical information, mitigating the risk of targeted breaches. Experimental evaluations demonstrate its effectiveness, with the proposed approach achieving Peak Signal to Noise Ratio (PSNR) values between 51.71 dB and 75.59 dB, and Structural Similarity Index Measure (SSIM) scores reaching up to 0.99. These metrics confirm that the stego images maintain high visual fidelity and diagnostic reliability. Overall, EPR-Stego outperforms several existing techniques, offering a robust and secure solution for medical data transmission. By combining imperceptibility, security, and quality preservation, the framework addresses the pressing need for reliable protection of patient information in telemedicine environments