Deep Learning-Based Computer-Aided Detection and Diagnosis System for Malignant Biliary Stricture (With Video)

Enhanced Colon Cancer Detection and Classification with Deep Learning Techniques: A Comparative Analysis

Colon cancer represents a growing universal issue related to health, with prompt and accurate detection essential for indispensable to improving outcomes of people's health. Standard approaches, like colonoscopy and histopathology, while useful, tend to be invasive, time-intensive and prone to human interpretative bias. Recent developments in deep learning (DL) have facilitated the creation of automated systems that improve the precision, speed and uniformity of colon cancer diagnosis and categorisation. This paper offers a thorough comparative examination of various advanced DL models, including ResNet, DenseNet and MobileNet, applied to multi-modal imaging datasets consisting of colonoscopy images. Quantitative findings indicate exceptional accuracy, precision and recall in diagnostic tasks, with MobileNet DL models outperforming in tumour diagnosis and grading than other peer groups.

A deep learning-based framework for the malignancy analysis of thyroid lesions in contrast-enhanced ultrasound videos.

BACKGROUND AND OBJECTIVE Contrast-enhanced ultrasound (CEUS) is widely used for evaluating thyroid nodule malignancy, but conventional time-intensity curve (TIC) analysis is labor-intensive and operator-dependent. This study proposes LSTAC, an automated framework for nodule segmentation and TIC analysis in CEUS videos. METHODS LSTAC integrates an improved YOLOv5-based segmentation network with a peak intensity frame (PIF) extraction algorithm to enable automatic nodule localization, TIC generation, and PIF identification. The framework was trained using CEUS data from 623 patients collected across three hospitals and evaluated on both internal and external validation cohorts. RESULTS LSTAC achieved 3-10× higher efficiency than VueBox in PIF extraction while maintaining strong temporal accuracy (0.94, 0.77, 0.79) and structural similarity (SSIM: 0.80, 0.60, 0.67). In malignancy prediction based on PIF features, LSTAC outperformed VueBox in two of three validation sets, with AUCs of 0.8279 vs. 0.8226 and 0.8000 vs. 0.7000. CONCLUSION LSTAC provides an efficient and accurate solution for automated CEUS analysis, reducing manual workload and improving consistency in thyroid nodule assessment, with potential for clinical application.

An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy.

AppendiGrade: An XAI-Enhanced Deep Learning Framework for Grading Appendicitis in Ultrasound with Gaussian Blur and Grad-CAM

An advanced system capable of automatically detecting complicated appendicitis from ultrasound images was developed and was explained with gradient-weighted class activation mapping (Grad-CAM), which creates a heatmap of the regions responsible for the model's prediction of the infected areas.

AUTOMATED DETECTION OF TUBERCULOSIS FROM CHEST X-RAY IMAGES USING DEEP LEARNING

One of the most common and deadly infectious illnesses in the world is still tuberculosis (TB), especially in developing nations with inadequate healthcare systems. In order to stop the spread of tuberculosis and enhance patient outcomes, early identification and diagnosis are essential. In this study, we present a deep learning-based system that uses chest X-ray pictures to automatically detect tuberculosis. Despite the difficulties of limited dataset availability, the system uses transfer learning using MobileNetV2 and DenseNet architectures to classify chest Xrays as either TB-positive or Healthy, reaching notable accuracy. To increase model generalisation and image quality, pre-processing methods like Contrast Limited Adaptive Histogram Equalisation (CLAHE) and sophisticated data augmentation approaches are used. The trained model is then implemented as a Flask web application, offering a user-friendly interface with features like secure login, image upload and preview, prediction results with probability scores, and performance metrics visualisation like accuracy curves, confusion matrices, and ROC curves. The suggested framework shows how deep learning can be used to create scalable, dependable, and affordable diagnostic tools to help radiologists and other medical professionals with TB screening and diagnosis.

Reducing False-Negative Risk in Automated Pneumonia Screening Using Attention-Guided Deep Learning

Pneumonia continues to be a major source of morbidity and mortality worldwide, especially in children, the elderly, and people with impaired immune systems. Due to its low cost and widespread availability, chest imaging radiology is the most often utilised diagnostic modality; yet, accurate interpretation is difficult and heavily dependent on radiologist expertise, which can result in clinically significant missed diagnosis. While deep learning-based methods have demonstrated potential for automated pneumonia detection, many of the models now in use rely on global feature learning, have poor interpretability, and do not adequately address the danger of false negatives. An attention-enhanced deep learning framework for clinically accurate pneumonia identification from chest imaging radiology is proposed in this study.To enhance spatial feature representation and highlight diagnostically significant lung regions, the framework combines a self-attention mechanism with a pretrained VGG16 backbone. To guarantee stable optimisation and efficient task adaptability, a two-stage training approach is used. The suggested model is tested using a single experimental methodology against many cutting-edge convolutional neural network architectures. The suggested framework obtains an accuracy of 96.3%, recall of 98.0%, F1-score of 97.5%, and ROC–AUC of 0.972, according to experimental results. Significantly, compared to the baseline VGG16 model, false-negative predictions are decreased from 27 to 17.