Deep Learning-Based Classification for Grading of Respiratory Distress Syndrome on Neonatal Chest Radiographs.

Development and evaluation of a deep learning model for computer-aided diagnosis of neonatal pneumothorax on chest radiographs.

The model showed promising diagnostic performance for detecting pneumothorax on supine neonatal chest radiographs and underlying pulmonary abnormalities may influence model performance, and external validation is warranted before clinical implementation.

PulmoScan AI: An Explainable Deep Learning-Based Clinical Decision Support System for Multi-Class Lung Disease Detection Using Chest X-Ray Images

PulmoScan AI is proposed, a deep learning-based full-stack clinical decision support system for automated detection and classification of lung diseases from CXR images that integrates real-time prediction with confidence thresholding, Grad-CAM explainability, and an occupational risk assessment module.

An attention-based multimodal hybrid deep learning framework for pneumonia classification using chest X-rays and radiologist-annotated severity labels

Adequate classification of pneumonia is essential for timely disease management and effective resource allocation in healthcare systems. As pneumonia presents with varying severity and its assessment utilizing chest radiographs is challenging, specifically detecting MILD and NORMAL-PCR+ cases, where chest radiographs m...

Current Medical Imaging

Multicenter cross domain evaluation of CNNs and vision transformers trained on adult data for pediatric pneumonia screening

The evaluated convolutional neural networks and vision transformers showed comparatively favorable and stable cross-domain behavior, although no architecture eliminated the adult-to-pediatric domain shift, which underscores the need for further pediatric-specific external validation before clinical translation.

COMPARISON OF PERFORMANCE AND COMPUTATIONAL COMPLEXITY OF CNN AND RESNET50 FOR PNEUMONIA CLASSIFICATION

This study aims to compare the classification performance and computational complexity of a CNN and a pre-trained ResNet50 model using transfer learning for binary pneumonia classification on chest X-ray images and shows that the CNN outperforms the ResNet50 across all classification metrics.