International Journal of Imaging Systems and Technology最新文献

Medical imaging is a core component of modern healthcare, essential for early disease diagnosis and effective treatment planning. Deep learning has emerged as a powerful tool to enhance medical image analysis, particularly in tasks such as segmentation, which is essential for identifying and delineating anatomical structures. A notable segmentation challenge involves accurately detecting narrow and elongated features. A novel DenseUNet architecture, enhanced with asymmetric convolutional kernels and a squeeze-and-excitation block, is proposed. It is specifically designed to adapt to such shape characteristics. The iterated local search metaheuristic is employed to optimize the kernel size within a search space of $��{15}^2�$, and a squeeze-and-excitation block is integrated to enhance feature recalibration and network efficiency. The best-performing asymmetric kernel achieved a processing time 5423 s faster than that of the conventional kernels. The proposed architecture is evaluated using the Dice coefficient and benchmarked against state-of-the-art architectures using three databases (TMJ: Temporomandibular joints, DCA1: Coronary arteries, and ICA: Coronary arteries), achieving Dice scores of 0.7800, 0.8231, and 0.8862, respectively. These enhancements demonstrate improved segmentation performance and contribute to the development of more accurate and robust medical imaging tools.

This study proposes a novel diagnostic approach to retinal disease detection by combining deep learning-based segmentation with fractal dimension (FD) analysis on optical coherence tomography (OCT) images. Our primary goal is to enhance early detection of retinal diseases such as age-related macular degeneration (AMD), diabetic retinopathy (DR), central serious retinopathy (CSR), and macular hole (MH). We introduce the Attention U-shaped Network (AUNet), which builds upon the UNet architecture with Attention Gates (AGs) to improve focus on pathological structures in complex cases, achieving a high segmentation accuracy of 98.5% and a mean Intersection over Union (mIoU) of 0.91, outperforming existing models like UNet++ and DeepLabV3+. Coupled with Fourier and Higuchi FD analysis, our method quantitatively assesses the complexity of retinal layers, identifying structural patterns that serve as early indicators of neural degeneration. Statistical tests reveal significant differences in FD values between diseased and healthy groups, underscoring the predictive power of retinal layers like the retinal pigment epithelium (RPE), inner nuclear layer (INL), outer nuclear layer (ONL), and ellipsoid zone (EZ). This combined AUNet-FD approach represents an innovative tool for early diagnosis of retinal diseases, potentially enhancing clinical decision-making through precise, non-invasive analysis.

Diabetic retinopathy (DR), one of the most prevalent microvascular complications of diabetes, stands as a leading cause of vision loss globally. Due to its asymptomatic nature in early stages, delayed diagnosis and staging may result in irreversible visual impairment. Therefore, accurate and simultaneous lesion segmentation and stage classification of DR are of critical clinical importance. In this study, a two-stage, end-to-end, holistic framework is proposed for automated DR diagnosis. In the first stage, an Improved U-Net architecture enhanced with residual blocks and additional convolutional layers is employed to segment small and low-contrast lesions such as microaneurysms, hemorrhages, and hard/soft exudates with high precision. Model hyperparameters are optimized using the harmony search algorithm to enhance training efficiency. In the second stage, lesion-based weight maps obtained from the segmentation step are applied to fundus images from the APTOS dataset, generating enriched inputs for classification. A vision transformer (ViT)-based model, augmented with a Convolutional Block Attention Module (CBAM), is utilized to improve feature extraction. In addition, features derived from ViT are further refined using a graph convolutional network (GCN) and traditional machine-learning classifiers. The proposed approach achieves high performance in multi-class DR stage classification. Compared to existing studies, the framework demonstrates notable improvements in both segmentation and classification accuracy, offering a robust and generalizable solution for DR diagnosis.

Accurate segmentation of medical images is critical for disease diagnosis and treatment planning. However, challenges such as fuzzy boundaries, low contrast, and complex anatomical structures often hinder performance. We propose EGCF-Net, a novel U-Net-based architecture that integrates a hybrid encoder with an edge-aware graph-based attention network to address these limitations. The hybrid encoder integrates the Swin transformer and Kronecker convolution to capture global and local contextual dependencies. Additionally, skip connections are enhanced using an edge-aware graph-based attention module, which combines graph spatial attention and graph channel attention to dynamically model spatial correlations and edge-aware contextual affinities. This design leads to edge-enhanced boundary delineation and improved regional consistency. We evaluate EGCF-Net on four benchmark datasets (Synapse, Kidney Stone, ISIC 2016, and ISIC 2018), achieving Dice scores of 84.70%, 93.07%, 91.07%, and 88.62%, respectively, surpassing existing state-of-the-art methods. Quantitative and qualitative results further validate the efficacy and robustness of the proposed approach, highlighting its potential for advancing medical image segmentation.

Gastrointestinal disorders include diseases that negatively affect people's daily life and carry the risk of cancer. Therefore, accurate and early diagnosis of these diseases is important for treatment process of patients. Deep learning architectures, which have achieved significant success in medical image analysis, are effectively used in early diagnosis and diagnosis systems. Therefore, in this study, a new approach that achieves higher accuracy in the detection of gastrointestinal diseases by combining DenseNet and Vision Transformer models with stacking ensemble is proposed. As a result of the experiments, the proposed model achieved 99.06% accuracy in a single test and 98.64% accuracy on mean as a result of 5-fold cross-validation. The proposed approach shows promising accuracy and reliability as evidenced by the results of experiments on the KvasirV2 dataset, and has the potential to be an effective method for the detection of gastrointestinal diseases. To improve model interpretability, the Explainable AI technique Grad-CAM and attention map visualizations were used, allowing visual justification of the model's predictions and highlighting clinically relevant regions in endoscopic images. The model obtained by combining DenseNet and Vision Transformer models with the stacking ensemble method is expected to be an example for future studies in the field of health and image processing, especially gastrointestinal diseases.

The skin, the largest organ of the human body, is vulnerable to numerous pathological conditions collectively referred to as skin lesions, encompassing a wide spectrum of dermatoses. Diagnosing these lesions remains challenging for medical practitioners due to their subtle visual differences, many of which are imperceptible to the naked eye. While not all lesions are malignant, some serve as early indicators of serious diseases such as skin cancer, emphasizing the urgent need for accurate and timely diagnostic tools. This study advances dermatological diagnostics by curating a comprehensive and balanced dataset containing 9360 dermoscopic and clinical images across 39 lesion categories, synthesized from five publicly available datasets. Five state-of-the-art deep learning architectures—MobileNetV2, Xception, InceptionV3, EfficientNetB1, and Vision Transformer (ViT)—were systematically evaluated on this dataset. To enhance model precision and robustness, Efficient Channel Attention (ECA) and Convolutional Block Attention Module (CBAM) mechanisms were integrated into these architectures. Extensive evaluation across multiple performance metrics demonstrated that the Vision Transformer with CBAM achieved the best results, with 93.46% accuracy, 94% precision, 93% recall, 93% F1-score, and 93.67% specificity. These findings highlight the effectiveness of attention-guided Vision Transformers in addressing complex, large-scale, multi-class skin lesion classification. By combining dataset diversity with advanced attention mechanisms, the proposed framework provides a reliable and interpretable tool to assist medical professionals in accurate and efficient lesion diagnosis, thereby contributing to improved clinical decision-making and patient outcomes.

Heart disease (HD) is still a major cause of death worldwide, which emphasizes the importance of early and precise prediction. This paper presents DenseEchoNet, a deep learning model that has been optimized with the Gazelle Optimizer Algorithm (GOA). The hybrid HD-ENN technique is used for balanced learning to solve class imbalance and high dimensionality, while squared exponential kernel-based PCA (SEKPCA) effectively reduces dimensionality. DenseEchoNet outperforms current baseline models with accuracies of 0.9795 as well as 0.9785, respectively, when tested on the HDHI and Cleveland datasets. XAI approaches, such as LIME and SHAP, improve model interpretability by offering distinct insights on feature contributions to HD risk. For early HD prediction, this system provides a straightforward, accurate, and efficient solution.