International Journal of Imaging Systems and Technology最新文献

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.

Skin lesion segmentation from dermoscopic images must be done accurately and consistently in order to diagnose diseases and arrange treatments. However, when dealing with issues like fuzzy lesion region boundaries, multiscale features, and notable variations in the lesion region's size, shape, and color, existing methods typically have high computational complexity and large parameter counts. They also frequently suffer from decreased segmentation accuracy due to inadequate capture of local features and global information. In this paper, a lightweight deep learning network based on high-performance adaptive attention is proposed to overcome these issues. Specifically, a deep convolutional neural network is introduced to capture local information. Meanwhile, we create a high-performance adaptive attention feature fusion module (EAAF) that uses dynamic feature selection to achieve adaptive fusion of global information with multiscale local features. Furthermore, we created a reverse dynamic feature fusion module (RDFM) at the decoding stage to efficiently fuse features at various levels while taking into account the integrity and specifics of the lesion region to increase the precision of complex lesion region segmentation. We carried out in-depth tests on three publicly accessible datasets International Skin Imaging Collaboration (ISIC)-2016, ISIC-2018, and PH² to assess the method's efficacy and contrasted the outcomes with those of the most advanced techniques; the results confirmed that the suggested approach was superior.

Gastrointestinal (GI) bleeding, arising from various conditions, can be critical if untreated. Wireless capsule endoscopy (WCE) is a highly effective method for detecting GI bleeding, offering full visualization of the GI tract. However, the large number of images generated per patient poses challenges for clinicians, leading to prolonged analysis times and increased risk of human error. This emphasizes the need for computer-aided diagnosis systems. In this study, we introduce SEA-Net (Structured Efficient Attention Network), a novel deep learning network for detecting bleeding regions in WCE images. SEA-Net integrates a Convolutional Block Attention Module (CBAM) with long skip connections to enhance gradient flow and improve blood region localization. The EfficientNet-B4 encoder improves feature extraction efficiency and generalizability. A five-fold cross validation demonstrates consistent performance, while generalization tests, including precision-recall curves, ROC curves, and F1 measure, further validate the model's robustness. Minimal performance degradation was observed when the training data was reduced from 80% to 20%. Experimental results show that SEA-Net achieves a Dice score of 93.64% and an IoU score of 88.61% on a publicly available WCE dataset, outperforming state-of-the-art models and highlighting its strong potential for clinical application.

Heart disease remains a leading global health concern, necessitating accurate and interpretable risk prediction models for effective clinical decision-making. Accurate heart disease risk prediction is crucial for preventive healthcare, yet traditional machine learning models often struggle with the inherent uncertainty and nonlinear patterns in medical data. While deep neural networks (DNNs) excel at feature extraction, their lack of interpretability limits clinical utility, whereas fuzzy inference systems (FIS) offer transparency but lack hierarchical learning capabilities. To bridge this gap, we propose a novel Deep Fuzzy Inference System (DFIS) that integrates DNNs and FIS into a unified architecture, combining the strengths of both approaches. The DFIS leverages a weighted fusion mechanism to combine probabilistic outputs from an Adam Cuckoo Search-optimized DNN and a trapezoidal membership-based FIS, enabling simultaneous high accuracy and interpretability. The performance is evaluated on the Cleveland heart disease dataset; the DFIS achieves 97.2% accuracy, outperforming a standalone DNN (95.4%) and ANFIS (91.7%) under the same experimental conditions while providing clinically actionable risk stratification into normal, less critical, and very critical categories.

This study presents a high performance hybrid deep learning model for the classification of 14 lung diseases using chest X-ray (CXR) images. Manual evaluation of CXR images is labor-intensive and prone to human error. Therefore, automated systems are required to improve diagnostic accuracy and efficiency. Our model integrates ResNet18 and EfficientNet-V2-S architectures, combining residual connections with efficient scaling to achieve high accuracy while maintaining computational efficiency. Trained on the NIH ChestX-ray14 dataset, comprising 112 120 images across 14 disease classes, the model mitigates class imbalances with extensive data augmentation techniques. Achieving an impressive average AUC of 0.872, the model outperforms previous approaches. This performance was enhanced by a refined, anatomically-aware data augmentation strategy that improved the model's robustness and clinical relevance, particularly in challenging disease categories such as Pneumothorax, Emphysema, and Hernia. To further validate its generalizability, the proposed model was tested on three additional datasets for pneumonia, COVID-19, and tuberculosis. The results demonstrate superior performance, achieving an accuracy of 0.958, F1 score of 0.944, and ROC AUC of 0.989 for pneumonia; an accuracy of 0.974, F1 score of 0.969, and ROC AUC of 0.995 for COVID-19; and an accuracy of 0.999, F1 score of 0.999, and ROC AUC of 0.999 for tuberculosis. These outstanding results confirm the robustness and clinical applicability of the model across diverse datasets. This research introduces a reliable and efficient diagnostic tool that enhances the potential of automated lung disease classification. By alleviating radiologists' workload and promoting timely, accurate diagnostic outcomes, the model contributes significantly to medical imaging applications and demonstrates its capacity for practical use in real-world clinical settings.

Macular edema is a retinal disorder that can lead to significant vision loss, underscoring the need for accurate and intelligent automated diagnosis. However, its subtle manifestations in color fundus photography (CFP) pose considerable challenges for conventional deep learning models. In this work, we propose a novel diagnostic framework that integrates Dempster–Shafer (D–S) evidence theory—a principled approach for uncertainty quantification and multi-source information fusion—with the advanced Mamba architecture. The proposed method employs a dual-branch network to selectively enhance and extract discriminative features from both bright and dark regions of fundus images. These features are dynamically aligned and fused via an Adaptive Multi-Branch Feature Synthesis (AMFS) module. To model long-range dependencies and aggregate complementary information from multiple scanning views, we introduce a multi-scan Mamba module, whose outputs are further fused using a principled D–S evidence mechanism. This synergistic integration of mathematical theory and deep learning not only reduces information redundancy but also enables confidence-aware automated decision-making. Extensive experiments on three retinal image datasets—IDRiD, Messidor, and a proprietary clinical cohort—demonstrate that our framework consistently outperforms state-of-the-art methods in terms of accuracy, F1-score, and robustness. These results highlight the promise of combining evidence theory with modern deep learning for challenging medical image analysis tasks.

Electroencephalogram (EEG) plays a vital role in identifying the neural activities and human behaviors. EEG is a high-temporal-resolution signal that is contaminated and influenced by various external factors and artifacts. It is very difficult to identify and analyze the EEG signal when it is contaminated by artifacts. The impact of artifacts in EEG signals became an open challenge in EEG signal processing. Even though many techniques and methods are available to remove the artifacts in EEG, analyzing the contaminated EEG signals remains a significant challenge. The signal is polluted by different artifacts like eye blink, muscle activities, environmental interfaces, and so on. Compared with other artifacts and noises, task-specific artifacts such as motor movement and motor imagery have a greater impact on EEG signals due to their direct inference on cognitive signals, and isolating task-specific artifacts from the EEG signal is difficult. To overcome these challenges, a novel hybrid task-specific Dynamic Multistage k-Means Clustering algorithm (TS-DMKC) has been proposed that detects and extracts the processed EEG signal, which will be helpful for all the EEG applications. Initially, the motor movement and motor imagery artifacts are distinguished using the Fast ICA by applying the filtering threshold. Then, a multistage k-means clustering algorithm is employed to isolate the artifacts in different clusters dynamically, and the cluster effectiveness is calculated by using the silhouette score. This proposed novel hybrid algorithm will be useful in various EEG applications like neuroscience, medical diagnosis, brain–computer interface, authentication, brain signal analysis, and gaming. The experimental results demonstrate that the proposed algorithm TS-DMKC achieved a classification accuracy of 94.2% using the Physionet motor movement/imagery dataset, reducing task-dependent variability and offering more robust and reliable EEG systems.