Journal of Electronics, Electromedical Engineering, and Medical Informatics

HAREN: A Hybrid Attention Residual Ensemble Network for PCOS classification and Prediction

2026-05-09T17:47:05+07:00

Polycystic Ovary Syndrome (PCOS) is one of the most prevalent endocrine disorders affecting women of reproductive age and is a leading cause of infertility. Ultrasound imaging is widely used for PCOS diagnosis; however, visual assessment of ovarian morphology is highly subjective, time-consuming, and dependent on clinical expertise. Quality differences in ultrasound images, very near to similar visual patterns among PCOS and NOT PCOS images, and noise in the images increase the threat of improper diagnosis. These problems suggest a need for an accurate, automatic, and computer-assisted PCOS diagnostic system. This research aims to create a deep learning-assisted automatic PCOS diagnostic system which can detect and classify the Polycystic Ovary Syndrome from the gray-scale ultrasound ovarian images. In addition to high classification accuracy, the proposed framework incorporates an explicit explainability pipeline that highlights diagnostically relevant ovarian regions, such as follicular distributions and stromal patterns, thereby supporting clinically interpretable decision making. The proposed HAREN framework addresses the limitations of single backbone models, and attention augmented variants, such as vanilla ResNet50 and ResNet50 with hybrid attention by leveraging ensemble learning and residual feature fusion. HAREN combines three architecturally diverse and complementary pretrained CNN backbones (ResNet50, DenseNet121, and EfficientNetB0) to enhance feature diversity. In addition, a novel hybrid attention mechanism combining channel, spatial, and cross-scale attention is introduced to emphasize diagnostically relevant ovarian regions. A residual fusion strategy is employed to preserve discriminative features and stabilize training, and an explicit explainability pipeline is incorporated to support Grad CAM-based visual interpretation. This network first converts the ultrasound grayscale ovarian images to RGB , followed by the extraction of important features applying backbones, which are augmented with attention mechanisms. The network, trained with categorical crossentropy loss, was evaluated using comprehensive performance metrics on 11,784 ultrasound images (6,784 PCOS and 5,000 NOT PCOS). HAREN achieved 99.33% accuracy, 98.96% precision, 98.97% recall, 98.96% F1 score, and an AUC of 99.93%, outperforming conventional models. Overall, it delivers an accurate, reliable, and interpretable solution for automated PCOS detection, demonstrating strong potential for clinical decision support systems

Predicting the Severity of Thyroid Nodules with YOLOv8 and CA+LSR Architecture

2026-05-16T11:02:22+07:00

The rise in thyroid cancer has significantly increased the burden on radiologists to diagnose thyroid nodules using sonography accurately. To address this challenge, a highly precise and efficient automatic computer-aided diagnosis system is needed. A retrospective analysis was conducted on a dataset consisting of 200 ultrasound images from 161 patients (84 benign and 77 malignant) at Wenzhou Central Hospital. This study presents an enhanced version of the You Only Look Once version 8 (YOLOv8) neural network, specifically designed to improve the accuracy of thyroid nodule diagnosis. YOLO has been objective in handling the required elements from the given input images or frames, and the article discusses the extensive benefits of the same. The proposed network incorporates a Coordinate Attention (CA) module and a Label Smoothing Regularization (LSR) module, which facilitate the extraction of positional information and enhance overall performance. The improved neural network demonstrates high accuracy in identifying lesion areas and classifying nodule types, achieving a mean average precision (mAP) of 90% with an average inference time of 8 milliseconds on the test dataset. The ablation experiment revealed that incorporating the CA and LSR modules adds 1.2 milliseconds of computational time per image while providing a significant 4.1% improvement in mean average precision (mAP). Compared with state-of-the-art networks, the enhanced YOLOv5 network performed exceptionally well in diagnosing benign and malignant thyroid nodules, even with a limited dataset. Furthermore, its high accuracy and efficiency suggest potential applicability to other sonographic diagnostic tasks, aiding radiologists in improving diagnostic accuracy and patient outcomes.

Quantum-Inspired Feature Engineering and Explainable AI for Robust Heart Disease Classification

2026-05-18T20:27:53+07:00

Early and accurate prediction of cardiovascular disease is essential to improve patient outcomes and reduce healthcare costs. This research presents a hybrid classical–quantum machine learning framework for heart disease prediction using the Cleveland dataset. The proposed pipeline integrates advanced feature engineering, bio-inspired optimization, and quantum-inspired learning to improve classification performance and interpretability. The system applies multiple feature selection techniques followed by a hybrid feature fusion strategy. Orthogonal Component Analysis is then used for dimensionality transformation, while quantum-inspired feature mapping simulates quantum state coding. A feature selection mechanism based on a Genetic Algorithm optimizes the subset of features. Classical and quantum machine learning models are evaluated, including Random Forest, Gradient Boosting, K-Nearest Neighbors, Logistic Regression, Quantum Support Vector Classifier, Variational Quantum Classifier, Quantum KNN, and Quantum Neural Networks. Model performance is evaluated using accuracy metrics. To ensure transparency and trustworthiness, explainable AI techniques such as SHAP, LIME and DiCE are integrated to provide local and global interpretability of predictions. Experimental results demonstrate that the proposed hybrid framework improves predictive performance by achieving 90% accuracy compared to traditional machine learning approaches, while maintaining model explainability. The model achieved an overall accuracy of 90%, indicating strong predictive capability in cardiovascular disease risk classification. A detailed analysis of class-wise performance shows that for Class 0, the model obtained a precision of 0.85, a recall of 0.97, and an F1-score of 0.90, demonstrating excellent ability to correctly identify negative cases with minimal false negatives. For Class 1, the model achieved a precision of 0.96, a recall of 0.84, and an F1-score of 0.90, indicating high confidence in positive predictions, though with slightly lower recall compared to Class 0. This study highlights the potential of combining classical feature engineering, evolutionary optimization and quantum-inspired learning for next-generation medical decision support systems. The integration of quantum-inspired techniques also provides a promising direction for improving computational efficiency and model robustness in healthcare analytics. The findings suggest that hybrid classical–quantum learning approaches can support clinicians in making faster and more reliable diagnostic decisions.

Classification of Lenke Scoliosis using GLCM Feature Extraction and Support Vector Machine

2026-05-20T17:36:50+07:00

Lenke scoliosis is a spinal deformity that is classified into six types by the Lenke classification system. Traditionally, clinicians undertake classification based on manual visual examination of X-ray images, which is time-consuming, requires high skill and is subject to errors caused by human fatigue. To overcome these constraints, the current work presents an automated and reliable classification system to boost the efficiency and accuracy of diagnosis. The method is based on the application of the Grey Level Co-occurrence Matrix (GLCM) for the feature extraction and of a Support Vector Machine (SVM) classifier. The main contribution is the optimisation of SVM kernel functions (Quadratic, Cubic and Coarse Gaussian) using advanced pre-processing methods to achieve very good accuracy while preserving compute efficiency suitable for clinical applications. The approach combines picture pre-processing (grey scale conversion, resize, contrast improvement by adaptive histogram equalisation, segmentation, augmentation) and GLCM-based feature extraction and classification using multiple SVM kernels. The model's performance is evaluated based on accuracy, precision, recall, F1 Score, and execution time. The testing results demonstrate that the Quadratic SVM has the best classification accuracy of 92.26% with a processing time of 20.44 seconds, which outperforms the Cubic SVM (90.97%, 19.30 seconds) and the Coarse Gaussian SVM (60.64%, 21.70 seconds). The results show that the quadratic SVM has the optimum compromise between accuracy and processing efficiency. In conclusion, the proposed GLCM-SVM approach has tremendous potential to support doctors in the automatic categorisation of Lenke scoliosis, improving the accuracy and speed of diagnosis without requiring large computational resources. In future work, we will aim to expand the dataset and include additional features to further improve the model's resilience and generalisability.

Comparative Analysis of Attention Mechanisms in Pix2Pix for Multimodal MRI Fusion

2026-06-01T12:33:35+07:00

Medical image fusion (MIF) is a key technique in medical imaging, which combines complementary information from different imaging modalities, thereby improving the accuracy of diagnosis, particularly for lesion detection and treatment planning. Deep learning has significantly advanced this area, with the development of generative models and transformers leading to improvements in fidelity and accuracy, although the study of the influence of attention mechanisms on these models remains limited to a single type or a single architectural placement. This paper offers an analytical examination of the architectures of Pix2Pix with three attention mechanisms (spatial attention, channel attention (Squeeze-and-Excitation), and self-attention), where they are tested in three different placement strategies (encoder-only, decoder-only, and encoder-decoder), using the BraTS2020 dataset, with training supervised by a pseudo-ground-truth derived from arithmetic averaging. We fused six MRI modality pairs (FLAIR-T1, FLAIR-T1ce, FLAIR-T2, T1-T1ce, T1-T2, T1ce-T2), evaluating them using different metrics, including SSIM, PSNR, NMI, Entropy, and Q^AB/F. Results show that, in all cases, attention integration can significantly improve the quality of fusion over baseline methods, including cGAN and standard Pix2Pix. Spatial attention with encoder-decoder placement shows the best results, with SSIM values up to 0.91 and PSNR superior to 25 dB for the heterogeneous modality pair FLAIR-T1. Similarly, channel and self-attention demonstrate their effectiveness, especially with encoder-decoder placements. Based on these findings, attention-based fusion systems can be practically designed in a way that enhances MMIF, and the importance of designing attention in accordance with the nature of the modality is emphasized for optimal fusion performance. Our study demonstrates its effectiveness and may serve as a foundation for future research.

A Cross-Scale Spatial–Channel Attention Inception Network for Efficient Medical Image Segmentation

2026-06-01T13:02:29+07:00

Medical image segmentation plays a crucial role in modern computerized diagnosis, as accurate delineation of anatomical structures directly impacts clinical decision-making and treatment planning. However, segmenting anatomically complex regions at a fine-grained level remains challenging, especially when computational efficiency is a key requirement. To address these challenges, the authors propose a novel, lightweight medical image segmentation framework, CSA-IncepLiteNet, designed to achieve high segmentation accuracy without imposing a significant computational burden. The CSA-IncepLiteNet architecture integrates two key innovations: cross-scale feature extraction and unified spatial channel attention learning. Central to this framework is the newly introduced Cross-Scale InceptionLite module, which efficiently captures multi-scale contextual information. This module is built using depth-wise separable convolutions and point-wise convolutions, enabling effective feature extraction while significantly reducing the number of trainable parameters. By learning features across multiple spatial scales, the network can better represent anatomically complex structures present in medical images. In addition, the authors propose a Cross-Scale Spatial Channel Attention (CSA) module that jointly models spatial saliency and channel-wise interdependencies within a unified attention-learning paradigm. This dual attention mechanism allows the network to focus on the most informative regions and feature channels simultaneously, leading to improved segmentation precision. The performance of CSA-IncepLiteNet was evaluated on the BUSI breast ultrasound dataset and multiple CT image modality-based datasets. Experimental results demonstrate that the proposed framework consistently outperforms existing state-of-the-art methods across all evaluated datasets. Notably, CSA-IncepLiteNet achieves an accuracy of 92.1% and a Dice coefficient of 82.94% on the BUSI dataset, while utilizing over 26 million fewer parameters than a conventional U-Net. These results highlight the model’s effectiveness, robustness, and suitability for resource-constrained medical imaging applications.

Hybrid CNN-Transformer Architecture for Robust Liver Tumor Segmentation in 2D CT Slices

2026-06-01T23:30:51+07:00

Liver tumor segmentation from CT scans is a task affected by class imbalance, low contrast, and small lesion size. Manual segmentation is time-consuming and also suffers from inter-observer variability. We propose a 2D CNN-Transformer model with 20.3M parameters in an encoder–decoder structure with four transformer layers (8 heads, 2048 feedforward dimension). The model processes 2D axial slices due to GPU memory limits. The loss function combines Cross-Entropy, Dice, and Focal losses with α = 0.25 and γ = 2.0. Preprocessing includes CLAHE (clip limit = 2.0, 8×8 tiles) and gamma correction (γ = 1.2). From the LiTS dataset (131 volumes), 11 volumes with 1,688 slices were selected based on tumor presence, annotation quality, and artifact removal. A patient-level split of 80% for training, 10% for validation, and 10% for testing was used to prevent data leakage. The model achieved liver Dice = 0.916 ± 0.122 and tumor Dice = 0.810 ± 0.304. The 95% confidence intervals using bootstrapping (1,000 resamples) were [0.897–0.934] for liver and [0.765–0.856] for tumor. Best validation results at Epoch 98 were liver Dice = 0.938, tumor Dice = 0.823, and accuracy = 0.992. Pixel accuracy was 99.20% and was not used as the main metric due to class imbalance, where background pixels exceed 90%. An ablation study showed that CLAHE and gamma correction improved tumor Dice by 8.6% and liver Dice by 3.3% compared to a baseline without preprocessing. The model shows performance for liver tumor segmentation on a LiTS subset. External validation on the full dataset and multi-center data is required before clinical use

Intelligent Fusion of Multi-Modal Medical Imaging: A Comprehensive Review of Methods, Challenges, and Clinical Integration

2026-05-19T11:15:15+07:00

Multimodal Medical Imaging Fusion (MMIF) is defined as the incorporation of information from multiple imaging modalities in a way that is mutually supplementary, thereby addressing limitations associated with using a single imaging modality to evaluate a patient and increasing diagnostic accuracy. Further, this review provides a dedicated synthesis of deep learning architectures in MMIF, examining CNN-based hybrids, attention-enhanced transformers, GAN-driven unsupervised fusion, and emerging diffusion models. The state of the art in MMIF can be classified into three levels of fusion: (1) pixel level, fusion of raw pixel intensity values to preserve spatial detail; (2) feature level, features are derived from textures, edges, and region-of-interest (ROI) descriptors; (3) decision level, fusing independent outputs of each source using ensemble or rule-based methods to produce a single, integrated output from all sources, potentially improving interpretability of the integrated output. The use of AI algorithms improves fusion outcomes by yielding higher-quality results. However, clinicians' confidence in deep-learning-based models is limited due to their inability to generalise across multiple scanners, protocols, and medical systems. This analysis demonstrates that clinical AI systems must be developed with interpretability as a core attribute, to provide an explanation of how each modality is contributing to the final decision, and to establish a fusion policy that preserves the ability to make accurate diagnostic determinations based on fused images. In addition to developing more sophisticated algorithms, future developments in MMIF will require collaborative partnerships between developers and clinicians to develop fused images into reliable diagnostic tools to be used in precision medicine.