International Journal of Imaging Systems and Technology最新文献

Diabetic retinopathy (DR) is a primary reason for visual impairment and blindness in individuals with diabetes worldwide. Timely detection of DR is essential to prevent vision loss in diabetics. However, noise and limited model transparency often compromise the accuracy of diagnosing retinal fundus images. Noise and interpretability are the two main challenges occurring in imaging datasets, overshadowing concerns such as class imbalance or device variability. These distortions are present in all datasets and devices, reducing the clarity of diagnostic signals at the pixel level and often obscuring early lesions within background noise. Addressing these challenges, this research introduces an innovative model called Explainable MINet-ViT, which combines advanced noise reduction techniques with explainable deep learning for more reliable identification of DR. The model incorporates a multi-level denoising network (MINet), modified by a noise-specific pre-processing module using a Variance-Stabilizing Transform (VST) and deep residual feature mapping. A hybrid deep learning architecture that combines Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) is employed to extract both local and global spatial information. We apply explainability strategies, such as Grad-CAM and SHAP, to ensure clinical interpretability by identifying the crucial retinal regions that influence model predictions. Quantitative and qualitative results show improved performance, robustness, and clinical applicability, achieving an accuracy of 97.6%, a sensitivity of 0.96, a specificity of 0.97, a Kappa of 0.92, and an AUC of 96.7%. Analyses of standard datasets reveal that our proposed model outperforms prior models in accuracy, noise robustness, and interpretability, rendering it exceptionally suitable for real-world clinical applications.

CT-guided thermal ablation is increasingly being used for the treatment of lung cancer; however, follow-up studies indicate that physicians' subjective intraoperative assessments often overestimate ablation success, potentially leading to incomplete treatment. To address this limitation, we developed Respiratory Differencing, a CT image-based intraoperative assistance system designed to improve ablation evaluation. The system first segments tumor regions in preoperative CT scans and then applies a multistage registration strategy to align them with intra- or postoperative CT/CBCT images, compensating for respiratory motion and treatment-induced anatomical changes. The system provides two key outputs. First, differential images are generated by subtracting the registered preoperative scan from the intraoperative scan, enabling direct visualization and quantitative comparison of pre- and posttreatment regions. These registered images, together with tumor masks, allow physicians to assess the spatial relationship between tumor and ablation zones—even when the tumor is no longer visible in postablation scans. Second, the system computes a quantitative Ablation Effectiveness Scale (AES) that measures the spatial discrepancy between the tumor region and the ablation zone, offering an objective index of treatment adequacy. By accounting for complex pulmonary deformations and integrating pre- and intraoperative data, this system enhances quality control in ablation procedures. In a retrospective study of 35 clinical cases, Respiratory Differencing significantly outperformed conventional subjective assessment in detecting under-ablation during or immediately after treatment, underscoring its potential to improve intraoperative decision-making and patient outcomes.

Breathing regulation is centralized in the lungs, ensuring that all body cells receive oxygen. Simultaneously, they filter the air to stop pathogens and unnecessary chemicals from entering the body. Specialized defence systems shield the lungs in the human body. Nevertheless, they are insufficient to remove the chance of developing several lung-related diseases. Inflammation, infections, or even more severe conditions can affect the lungs, potentially leading to the development of a malignant tumor. An efficient healthcare monitoring system is used in the present research to identify people who are at a high risk of developing lung cancer and to provide early treatments to prevent long-term problems. To effectively predict lung cancer, a combined deep-learning intelligence system is proposed in this research. The correct features are selected using the Relief and Improved Least Absolute Shrinkage and Selection Operator (LASSO) techniques, reducing the feature dimension by 42.7%. Then, the deep learning-based improved GoogleNet model is used to extract deep discriminative features of 1024 dimensions, which enhances the feature representation capacity. Finally, the deep learning-based modified VGG19 model predicts lung cancer disease. On the UCI machine learning repository dataset (276 samples), the proposed model achieves 98.85% accuracy, with a 98.78% precision, 98.82% recall, 98.75% F1-score, and 96.52% AUC. The proposed combined intelligent system outperformed cutting-edge techniques regarding feature extraction and lung cancer prediction accuracy, showing a 4%–6% improvement over existing models. The developed smart and intelligent model is sustainable, saving computational resources by approximately 17% and human resources by reducing manual diagnosis time by over 60%. It is safe and helps medical professionals make accurate and timely decisions about lung cancer detection.

Interstitial Lung Diseases (ILD) are typically progressive diseases characterized by poor prognosis due to the inflammation and fibrosis affecting lung tissue. ILD is diagnosed through the identification of specific patterns or combinations of patterns that occur in various regions of the lung. This study employs High-Resolution Computed Tomography (HRCT) scans from the MedGIFT database to classify the patterns causing ILD on a slice-based. To achieve this, the pretrained models and a base Convolutional Neural Network (CNN) are utilized to provide a slice-based classification of ILD patterns in five, six, and seven classes. Four different pretrained models, namely VGG, DenseNet, MobileNet, and EfficientNet, are employed, and the performance impact of two training strategies, namely transfer learning and fine-tuning, is also evaluated. In the study, the effects of four different input resolution types on classification performance were investigated. The features extracted from the pretrained models and a base CNN are classified using a fully connected Artificial Neural Network classifier. The classification performance was further examined using two data augmentation methods for the most successful model and input resolution types. With the EfficientNetB0 pretrained model, classification results of five, six, and seven classes are obtained as 98.070%, 90.819%, and 87.781% F-score, respectively. Additionally, the computational costs and time complexity of all model combinations are analyzed, and their characteristics are comparatively discussed. This study contributes to the limited body of research on slice-based classification and advances clinical practice by facilitating the automatic detection of patterns on HRCT slices as a preprocessing step. Furthermore, the MedGIFT database is systematically analyzed in terms of slice and Region of Interest numbers across different pattern types, offering meaningful insights to support and guide its use in future research.

Diabetes Mellitus (DM), including Type 1 and Type 2, is a metabolic disorder caused by defects in insulin secretion or action. Non-invasive detection is more critical because invasive methods often lack data and have reduced accuracy, leading to poorer machine learning performance. This research proposes a new Octave-CenterNet with DenseNet-77 framework for efficient detection and classification of diabetes from Volatile Organic Compounds (VOCs). The method combines a rapid discrete curvelet transform with wrapping to capture prominent features quickly, uses octave convolution to preserve high and low-frequency patterns and enrich representations, employs CenterNet to detect acetone as a major biomarker, and leverages DenseNet-77 for gradient-efficient classification. Willow sled catkin optimization adaptively fine-tunes hyperparameters to further enhance performance. The model effectively distinguishes healthy individuals from diabetic patients and differentiates between Type 1 and Type 2 diabetes. Experimental results demonstrate excellent performance with 98.7% accuracy, 98% precision, 99.7% recall, and 99.34% F1 score, validating its robustness. Overall, this end-to-end, noise-resistant, and computationally efficient framework offers a technically advanced and practical solution for non-invasive diabetic detection.

To develop and validate a multimodal machine learning model for opportunistic osteoporosis screening in postmenopausal women using dental periapical radiographs. This retrospective multicenter study analyzed 3885 periapical radiographs paired with DEXA-derived T-scores from postmenopausal women. Clinical, handcrafted radiomic, and deep features were extracted, resulting in a fused feature set. Radiomic features (n = 215) followed Image Biomarker Standardization Initiative (IBSI) guidelines, and deep features (n = 128) were derived from a novel attention-based autoencoder. Feature harmonization used ComBat adjustment; reliability was ensured by intra-class correlation coefficient (ICC) filtering (ICC ≥ 0.80). Dimensionality was reduced via Pearson correlation and LASSO regression. Four classifiers—logistic regression, random forest, multilayer perceptron, and XGBoost—were trained and evaluated across stratified training, internal, and external test sets. A logistic regression model was selected for clinical translation and nomogram development. Decision curve analysis assessed clinical utility. XGBoost achieved the highest classification performance using the fused feature set, with an internal AUC of 94.6% and external AUC of 93.7%. Logistic regression maintained strong performance (external AUC = 91.3%) and facilitated nomogram construction. Deep and radiomic features independently outperformed clinical-only models, confirming their predictive strength. SHAP analysis identified DEXA T-score, age, vitamin D, and selected radiomic/deep features as key contributors. Calibration curves and Hosmer–Lemeshow test (p = 0.492) confirmed model reliability. Decision curve analysis showed meaningful net clinical benefit across decision thresholds. Dental periapical radiographs can be leveraged for accurate, non-invasive osteoporosis screening in postmenopausal women. The proposed model demonstrates high accuracy, generalizability, and interpretability, offering a scalable solution for integration into dental practice.

Lung cancer remains the leading cause of cancer-related incidence and mortality worldwide. Early detection of lung nodules is crucial for significantly reducing the risk of lung cancer. However, due to the high similarity in CT image features between lung nodules and surrounding normal tissues, nodules are often missed or misidentified during the detection process. Moreover, the diverse types and morphologies of nodules further complicate the development of a unified detection approach. To address these challenges, this study proposes a novel Feature Reconstruction-guided Multi-Scale Attention Network (FRMANet). Specifically, a Refined Feature Reconstruction Module is designed to effectively suppress redundant information while preserving essential feature representations of nodules, ensuring high sensitivity and enhanced representation capability for nodule regions during feature extraction. Additionally, a Multi-scale Feature Enhancement Attention mechanism is introduced, which utilizes an attention-based fusion strategy across multiple scales to fully capture discriminative features of nodules with varying sizes and shapes. Experimental results on the LUNA16 dataset demonstrate that the proposed FRMANet achieves superior detection performance, with a mAP of 0.894 and an F1 score of 0.923, outperforming existing state-of-the-art methods.

Identifying the primary tumor origin is a critical factor in determining treatment strategies for brain metastases, which remain a major challenge in clinical practice. Traditional diagnostic methods rely on invasive procedures, which may be limited by sampling errors. In this study, a dataset of 200 patients with brain metastases originating from six different cancer types (breast, gastrointestinal, small cell lung, melanoma, non-small cell lung, and renal cell carcinoma) was included. Radiomic features were extracted from different magnetic resonance images (MRI) and selected using the Kruskal–Wallis test, correlation analysis, and ElasticNet regression. Machine learning models, including support vector machine, logistic regression, and random forest, were trained and evaluated using cross-validation and unseen test sets to predict the primary origins of metastatic brain tumors. Our results demonstrate that radiomic features can significantly enhance classification accuracy, with AUC values reaching 0.98 in distinguishing between specific cancer types. Additionally, survival analysis revealed significant differences in survival probabilities across primary tumor types. This study utilizes a larger, single-center cohort and a standardized MRI protocol, applying rigorous feature selection and multiple machine learning classifiers to enhance the robustness and clinical relevance of radiomic predictions. Our findings support the potential of radiomics as a non-invasive tool for metastatic tumor prediction and prognostic assessment, paving the way for improved personalized treatment strategies. Radiomic features extracted from MRI images can significantly enhance the prediction of the main origin of the metastatic tumor types in the brain, thereby informing treatment decisions and prognostic assessments.

Patient demographic prediction involves estimating age, gender, ethnicity, and other personal characteristics using X-rays. This can help in personalized medicine and improved healthcare outcomes. It can assist in automated diagnosis for some diseases that exhibit age and gender-specific prevalence. It can also help in forensic science to identify individuals when demographic information is missing. Insights from deep learning can verify the gender and age of self-reported individuals through chest X-rays (CXRs). In this proposed work, we have deployed an artificial intelligence (AI) enabled model which focuses on two tasks: gender classification and age prediction from CXRs. For gender classification, the model combines ResNet-50 (CNN) and Vision Transformer (ViT) to leverage both local feature extraction and global contextual understanding for predicting gender and is called ViTCXRResNet. The model was trained and validated on an Amazon Web Services (SPR) dataset of 10702 images, split with an 80–20 ratio, which was evaluated with classification metrics to determine the model's behavior. For age prediction, extracted features from ResNet-50 were used with dimensionality reduction through principal component analysis (PCA). A fully connected feedforward neural network was trained on the reduced feature set to predict age. The classification and regression model achieves accuracy results of 93.46% for gender classification and 0.86 for the R² score for age prediction on the SPR dataset. For visual interpretation, explainable AI (Gradient-weighted Class Activation Mapping) was utilized to visualize and find out which parts of the image are prioritized for classifying gender. The proposed model yields high classification accuracy in gender detection and significant accuracy in age prediction. The model shows competitive accuracy compared to existing methods. Further, the demographic prediction stability of the model was proven on two different ethnic groups, such as the Japanese Society of Radiological Technology (JSRT) and Montgomery (USA) datasets.