Automated Differentiation of Hepatic Cysts and Metastatic Tumors Using a Deep Learning Ensemble Framework on CT Imaging in Low-Resource Areas

An Interpretable Machine Learning Model for the Differentiation of Liver Cysts and Liver Tumors Based on Computed Tomography (CT) Imaging

Background: Metastatic liver tumors (MLT) and parasitic liver cysts (PLC) are common liver conditions that often exhibit similar imaging characteristics, making accurate diagnosis challenging using imaging alone. This overlap can result in diagnostic errors and delayed treatment, particularly in resource-limited settings or when invasive procedures such as biopsies are not feasible due to risk or unavailability. This study aimed to develop a reliable and transparent machine learning approach to distinguish MLT from PLC using radiomic features derived from computed tomography (CT). Methods: We propose an explainable radiomics-based machine learning framework for the non-invasive, accurate, and interpretable discrimination of MLT and PLC, designed to assist radiologists in reducing diagnostic ambiguity and expediting patient management. This retrospective study included 30 adult patients, comprising 15 with liver metastases and 15 with pathologic hepatic cysts. Radiomic features were extracted from pre-treatment CT scans using PyRadiomics. Feature selection was performed using three complementary methods: Mutual Information, Lasso regression, and LightGBM importance ranking. HistGradientBoosting classifiers were then trained on each selected feature set. Results: Model performance was evaluated using 5-fold cross-validation and assessed with ROC AUC, accuracy, precision, recall, and F1-score. SHAP analysis was applied to interpret the models and identify key radiomic biomarkers. Statistical comparisons were performed using DeLong’s test for AUCs, McNemar’s test for classification agreement, and paired t-tests for metrics such as accuracy and F1-score. The Mutual Information-based model achieved the highest mean AUC (0.9717 ± 0.0267), significantly outperforming the other models (p < 0.035). Key features contributing to classification included texture entropy, interquartile range, and gray level non-uniformity. Conclusion: We developed a robust and interpretable machine learning framework for differentiating metastatic liver tumors from parasitic liver cysts using CT-derived radiomic features. The integration of Mutual Information feature selection, ensemble learning, and SHAP explainability ensured high diagnostic accuracy, strong calibration, and transparency. The proposed framework demonstrates substantial clinical relevance and holds promise for real-world implementation.

Deep Learning-Based Lung Cancer Segmentation and Volumetric Analysis Using CT Imaging: A Comprehensive Survey.

A Deep Learning Approach for Automated Kidney Tumor Classification

A hybrid architecture in which ResNet50 is employed for localized spatial feature extraction, while Vision Transformer enables global contextual learning to automatically classify kidney tumors into multiple classes is proposed.

Deep learning-based detection of pediatric brain tumor presence on MRI

Abstract Introduction Pediatric cancer imposes a significant global burden, with ∼400 000 new cases annually. Magnetic resonance imaging (MRI) is a fundamental technique for timely diagnosis; however, interpretation can be subjective. In this context, convolutional neural networks (CNNs) have emerged as a promising tool for early and accurate detection of brain tumors in pediatric patients. Objective To develop and validate a CNN model for tumor detection tasks (tumor vs nontumor) of pediatric brain MRI images using T1-weighted imaging. Methodology T1-weighted brain MRI images from 285 pediatric patients were included (140 with confirmed tumors and 145 controls). A CNN architecture with convolutional layers, max-pooling, and dropout regularization was implemented. The model was trained and evaluated using cross-validation, employing the following metrics: accuracy, precision, recall, and F1-score. Model performance was compared across training configurations of 10-50 and 100 epochs to determine the optimal setup. Results The model trained for 40 epochs achieved the best overall performance, with a precision of 99%, a recall of 100%, and an F1-score of 99%. These metrics demonstrate excellent tumor detection while eliminating false negatives, a crucial aspect in pediatric oncology. Conclusion The CNN demonstrates strong potential as a diagnostic aid for detection brain tumors using pediatric MRI images. Its integration into clinical environments could contribute to more objective interpretation, optimize diagnostic workflows, and provide imaging follow-up before and after treatment.

Hybrid Deep Learning Methodology for Automated Hepatic Blood Vessel and Liver Tumor Analysis using CT Imaging

Segmentation of hepatic blood vessels and liver tumors, from computed tomography (CT) images is a crucial task in liver cancer diagnosis and surgical planning, vascular assessment, liver transplantation, and monitoring of liver cancer treatment. Although deep learning has seen many developments recently, current methods for liver segmentation fail to concurrently maintain high-resolution hepatic vascular structures while accurately segmenting tumors with variable shapes, sizes and contrast. While Conventional Convolutional Neural Network (CNN) models focus mainly on learning local spatial representations, Transformer models have a stronger focus on learning global contextual information, resulting in a lack of ability to comprehensively capture the complementary characteristics of features within a single framework. Considering the above challenges, this study proposes a novel Hybrid Deep Learning Methodology for Automated Hepatic Blood Vessel and Liver Tumor Analysis Using CT Imaging (HDLM-HTA). The proposed framework leverages the benefits of multi-scale convolutional feature extraction, dual attention-based feature refinement, global context learning with Vision Transformer, adaptive feature fusion, and multi-task segmentation in a single end-to-end pipeline to improve segmentation accuracy and robustness.The pre-processing stage of the proposed methodology consists of several steps: intensity normalization, bilateral filtering, extraction of Region of Interest (ROI) of the liver, resizing the CT image to the standard resolution and data augmentation to increase the image quality and promote the generalization of the model. Various receptive fields of a multi-scale CNN can then extract hierarchical spatial features, and a dual attention mechanism (channel attention and spatial attention) can highlight clinically relevant vessel and tumor regions to enhance the discriminative anatomical representation. Then, a global context learning module based on Vision Transformer is implemented to make use of the long-range anatomical dependency and guarantee vascular continuity. Using an adaptive feature fusion strategy, local features learnt from the affected liver tissue are fused with the globally learnt features and sent to the multi-task segmentation decoder, which is responsible for generating the hepatic blood vessel and liver tumour masks simultaneously. Optimization of the network is done by minimizing a hybrid loss function that consists of Dice Loss, Binary Cross-Entropy Loss and Focal Loss, which is suitable for class imbalance, yet still enhances segmentation performance.Experimental tests demonstrate that the proposed HDLM-HTA framework greatly improves the current CNN and hybrid deep learning models on various quantitative metrics including Dice Similarity Coefficient, Intersection over Union, Precision, Recall, F1-score, Hausdorff Distance and Average Surface Distance. It is a significant improvement to obtain multi-scale feature extraction, dual attention mechanism, transformer-based contextual learning, and adaptive feature fusion, which are essential for retaining fine branches of liver vessels and for defining the borders of complex liver tumors. These results show that the proposed automated liver image analysis framework is reliable, robust and clinically applicable. In summary, the proposed approach of HDLM-HTA is an effective computer-aided liver blood vessels segmentation and liver tumor segmentation method that can be used for liver cancer diagnosis, surgical planning, vascular mapping, treatment monitoring and precision medicine applications.

Advances in the application of deep learning in computed tomography imaging analysis of rare lung diseases

Diagnosis and management of rare lung diseases remain challenging owing to their low incidence, heterogeneous manifestations, and limited therapeutic options. High-resolution computed tomography (CT) is central to imaging assessment, yet conventional visual interpretation is subjective and lacks good reproducibility. Recent advances in artificial intelligence, especially deep learning, provide new approaches for automated, quantitative and objective chest computed tomography image analysis. This review summarizes deep learning applications across core stages of CT imaging analysis for rare respiratory diseases: image reconstruction and generation, lesion segmentation and detection, disease classification and diagnosis, and treatment response and prognosis prediction. With typical cases of rare lung diseases, we illustrate that deep learning models enable accurate quantification of imaging biomarkers, elevated diagnostic accuracy and optimized outcome stratification. Despite notable progress, key challenges remain in model generalization, interpretability, and clinical validation.