In this study, we explore the potential of ten quantitative (radiofrequency-based) ultrasound parameters to assess the progressive loss of collagen and proteoglycans, mimicking an osteoarthritis condition in ex-vivo bovine cartilage samples. Most analyzed metrics showed significant changes as the degradation progressed, especially with collagenase treatment. We propose for the first time a combination of these ultrasound parameters through machine learning models aimed at automatically identifying healthy and degraded cartilage samples. The random forest model showed good performance in distinguishing healthy cartilage from trypsin-treated samples, with an accuracy of 60%. The support vector machine demonstrated excellent accuracy (96%) in differentiating healthy cartilage from collagenase-degraded samples. Histological and mechanical analyses further confirmed these findings, with collagenase having a more pronounced impact on both mechanical and histological properties, compared to trypsin. These metrics were obtained using an ultrasound probe having a transmission frequency of 15 MHz, typically used for the diagnosis of musculoskeletal diseases, enabling a fully non-invasive procedure without requiring arthroscopic probes. As a perspective, the proposed quantitative ultrasound assessment has the potential to become a new standard for monitoring cartilage health, enabling the early detection of cartilage pathologies and timely interventions.

The stomach is one of the main digestive organs in the GIT, essential for digestion and nutrient absorption. However, various gastrointestinal diseases, including gastritis, ulcers, and cancer, affect health and quality of life severely. The precise diagnosis of gastrointestinal (GI) tract diseases is a significant challenge in the field of healthcare, as misclassification leads to late prescriptions and negative consequences for patients. Even with the advancement in machine learning and explainable AI for medical image analysis, existing methods tend to have high false negative rates which compromise critical disease cases. This paper presents a hybrid deep learning based explainable artificial intelligence (XAI) approach to improve the accuracy of gastrointestinal disorder diagnosis, including stomach diseases, from images acquired endoscopically. Swin Transformer with DCNN (EfficientNet-B3, ResNet-50) is integrated to improve both the accuracy of diagnostics and the interpretability of the model to extract robust features. Stacked machine learning classifiers with meta-loss and XAI techniques (Grad-CAM) are combined to minimize false negatives, which helps in early and accurate medical diagnoses in GI tract disease evaluation. The proposed model successfully achieved an accuracy of 93.79% with a lower misclassification rate, which is effective for gastrointestinal tract disease classification. Class-wise performance metrics, such as precision, recall, and F1-score, show considerable improvements with false-negative rates being reduced. AI-driven GI tract disease diagnosis becomes more accessible for medical professionals through Grad-CAM because it provides visual explanations about model predictions. This study makes the prospect of using a synergistic DL with XAI open for improvement towards early diagnosis with fewer human errors and also guiding doctors handling gastrointestinal diseases.

Effective treatment for brain tumors relies on accurate detection because this is a crucial health condition. Medical imaging plays a pivotal role in improving tumor detection and diagnosis in the early stage. This study presents two approaches to the tumor detection problem focusing on the healthcare domain. A combination of image processing, vision transformer (ViT), and machine learning algorithms is the first approach that focuses on analyzing medical images. The second approach is the parallel model integration technique, where we first integrate two pre-trained deep learning models, ResNet101, and Xception, followed by applying local interpretable model-agnostic explanations (LIME) to explain the model. The results obtained an accuracy of 98.17% for the combination of vision transformer, random forest and contrast-limited adaptive histogram equalization and 99. 67% for the parallel model integration (ResNet101 and Xception). Based on these results, this paper proposed the deep learning approach-parallel model integration technique as the most effective method. Future work aims to extend the model to multi-class classification for tumor type detection and improve model generalization for broader applicability.

Fine-grained visual classification is fundamental for medical image applications because it detects minor lesions. Diabetic retinopathy (DR) is a preventable cause of blindness, which requires exact and timely diagnosis to prevent vision damage. The challenges automated DR classification systems face include irregular lesions, uneven distributions between image classes, and inconsistent image quality that reduces diagnostic accuracy during early detection stages. Our solution to these problems includes MSCAS-Net (Multi-Scale Cross and Self-Attention Network), which uses the Swin Transformer as the backbone. It extracts features at three different resolutions (12 × 12, 24 × 24, 48 × 48), allowing it to detect subtle local features and global elements. This model uses self-attention mechanics to improve spatial connections between single scales and cross-attention to automatically match feature patterns across multiple scales, thereby developing a comprehensive information structure. The model becomes better at detecting significant lesions because of its dual mechanism, which focuses on both attention points. MSCAS-Net displays the best performance on APTOS and DDR and IDRID benchmarks by reaching accuracy levels of 93.8%, 89.80% and 86.70%, respectively. Through its algorithm, the model solves problems with imbalanced datasets and inconsistent image quality without needing data augmentation because it learns stable features. MSCAS-Net demonstrates a breakthrough in automated DR diagnostics since it combines high diagnostic precision with interpretable abilities to become an efficient AI-powered clinical decision support system. The presented research demonstrates how fine-grained visual classification methods benefit detecting and treating DR during its early stages.

This study developed a 5-year survival prediction model for gastric cancer patients by combining radiomics and deep learning, focusing on CT-based 2D and 3D features of the iliopsoas and erector spinae muscles. Retrospective data from 705 patients across two centers were analyzed, with clinical variables assessed via Cox regression and radiomic features extracted using deep learning. The 2D model outperformed the 3D approach, leading to feature fusion across five dimensions, optimized via logistic regression. Results showed no significant association between clinical baseline characteristics and survival, but the 2D model demonstrated strong prognostic performance (AUC ~ 0.8), with attention heatmaps emphasizing spinal muscle regions. The 3D model underperformed due to irrelevant data. The final integrated model achieved stable predictive accuracy, confirming the link between muscle mass and survival. This approach advances precision medicine by enabling personalized prognosis and exploring 3D imaging feasibility, offering insights for gastric cancer research.

Breast cancer remains a major cause of mortality among women, where early and accurate detection is critical to improving survival rates. This study presents a hybrid classification approach for mammogram analysis by combining handcrafted statistical features and deep learning techniques. The methodology involves preprocessing with the Shearlet Transform, segmentation using Improved Otsu thresholding and Canny edge detection, followed by feature extraction through Gray Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and 1st-order statistical descriptors. These features are input into a 2D BiLSTM-CNN model designed to learn spatial and sequential patterns in mammogram images. Evaluated on the MIAS dataset, the proposed method achieved 97.14% accuracy, outperforming several benchmark models. The results indicate that this hybrid strategy offers improvements in classification performance and may assist radiologists in more effective breast cancer screening.

This study aimed to develop a machine learning model based on Magnetic Resonance Imaging (MRI) radiomics for predicting early recurrence after curative surgery in patients with hepatocellular carcinoma (HCC).A retrospective analysis was conducted on 200 patients with HCC who underwent curative hepatectomy. Patients were randomly allocated to training (n = 140) and validation (n = 60) cohorts. Preoperative arterial, portal venous, and delayed phase images were acquired. Tumor regions of interest (ROIs) were manually delineated, with an additional ROI obtained by expanding the tumor boundary by 5 mm. Radiomic features were extracted and selected using the Least Absolute Shrinkage and Selection Operator (LASSO). Multiple machine learning algorithms were employed to develop predictive models. Model performance was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis, and calibration curves. The 20 most discriminative radiomic features were integrated with tumor size and satellite nodules for model development. In the validation cohort, the clinical-peritumoral radiomics model demonstrated superior predictive accuracy (AUC = 0.85, 95% CI: 0.74-0.95) compared to the clinical-intratumoral radiomics model (AUC = 0.82, 95% CI: 0.68-0.93) and the radiomics-only model (AUC = 0.82, 95% CI: 0.69-0.93). Furthermore, calibration curves and decision curve analyses indicated superior calibration ability and clinical benefit. The MRI-based peritumoral radiomics model demonstrates significant potential for predicting early recurrence of HCC.

Clinical two-dimensional (2D) MRI data has seen limited application in the early diagnosis of Parkinson's disease (PD) and multiple system atrophy (MSA) due to quality limitations, yet its diagnostic and therapeutic potential remains underexplored. This study presents a novel machine learning framework using reconstructed clinical images to accurately distinguish PD from MSA and identify disease-specific neuroimaging biomarkers. The structure constrained super-resolution network (SCSRN) algorithm was employed to reconstruct clinical 2D MRI data for 56 PD and 58 MSA patients. Features were derived from a functional template, and hierarchical SHAP-based feature selection improved model accuracy and interpretability. In the test set, the Extra Trees and logistic regression models based on the functional template demonstrated an improved accuracy rate of 95.65% and an AUC of 99%. The positive and negative impacts of various features predicting PD and MSA were clarified, with larger fourth ventricular and smaller brainstem volumes being most significant. The proposed framework provides new insights into the comprehensive utilization of clinical 2D MRI images to explore underlying neuroimaging biomarkers that can distinguish between PD and MSA, highlighting disease-specific alterations in brain morphology observed in these conditions.

Parkinson's disease (PD) is a devastating neurological ailment affecting both mobility and cognitive function, posing considerable problems to the health of the elderly across the world. The absence of a conclusive treatment underscores the requirement to investigate cutting-edge diagnostic techniques to improve patient outcomes. Machine learning (ML) has the potential to revolutionize PD detection by applying large repositories of structured data to enhance diagnostic accuracy. 133 papers published between 2021 and April 2024 were reviewed using a systematic literature review (SLR) methodology, and subsequently classified into five categories: acoustic data, biomarkers, medical imaging, movement data, and multimodal datasets. This comprehensive analysis offers valuable insights into the applications of ML in PD diagnosis. Our SLR identifies the datasets and ML algorithms used for PD diagnosis, as well as their merits, limitations, and evaluation factors. We also discuss challenges, future directions, and outstanding issues.

Brain tumors are a significant contributor to cancer-related deaths worldwide. Accurate and prompt detection is crucial to reduce mortality rates and improve patient survival prospects. Magnetic Resonance Imaging (MRI) is crucial for diagnosis, but manual analysis is resource-intensive and error-prone, highlighting the need for robust Computer-Aided Diagnosis (CAD) systems. This paper proposes a novel hybrid model combining Transfer Learning (TL) and attention mechanisms to enhance brain tumor classification accuracy. Leveraging features from the pre-trained DenseNet201 Convolutional Neural Networks (CNN) model and integrating a Transformer-based architecture, our approach overcomes challenges like computational intensity, detail detection, and noise sensitivity. We also evaluated five additional pre-trained models-VGG19, InceptionV3, Xception, MobileNetV2, and ResNet50V2 and incorporated Multi-Head Self-Attention (MHSA) and Squeeze-and-Excitation Attention (SEA) blocks individually to improve feature representation. Using the Br35H dataset of 3,000 MRI images, our proposed DenseTransformer model achieved a consistent accuracy of 99.41%, demonstrating its reliability as a diagnostic tool. Statistical analysis using Z-test based on Cohen's Kappa Score, DeLong's test based on AUC Score and McNemar's test based on F1-score confirms the model's reliability. Additionally, Explainable AI (XAI) techniques like Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-agnostic Explanations (LIME) enhanced model transparency and interpretability. This study underscores the potential of hybrid Deep Learning (DL) models in advancing brain tumor diagnosis and improving patient outcomes.