A comprehensive review of AI-based approaches for lung disease classification using chest X-ray images

AI-Based X-Ray Analysis for Early Thoracic Disease Detection

Thoracic diseases remain among the leading causes of death worldwide, with over 20.5 million cardiovascular and 3.5 million pulmonary deaths reported in 2021. Chest X-ray (CXR) diagnosis still relies heavily on radiologists, whose varying expertise can lead to slow, subjective, and inconsistent reports. Deep learning o...

Lung Disease Detection and Classification Based on AI, Deep Learning and Machine Learning: A Comprehensive Survey

Background: Lung diseases significantly contribute to substantial global morbidity and mortality, which pose considerable diagnostic challenges. Manual analysis of medical images like chest X-rays and CT scans is often subject to human error, requires specialized expertise, and is time-consuming. To build fully automat...

AI-Powered Multi-Disease Chest X-Ray Analysis And Explainable AI System Using Densenet121 and Grad-Cam

Chest X-ray imaging is widely used for examining abnormalities associated with the lungs and respiratory system. The increasing availability of medical image datasets has created opportunities for applying deep learning techniques to assist in the preliminary analysis of chest radiographs. However, classification of di...

Hybrid Deep Feature Learning with Grad-CAM-based Explainability for Multi-Disease Detection in Chest X-Ray Imaging

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.

Artificial intelligence in breast cancer imaging: a systematic review of detection, segmentation, explainability, and clinical translation

Artificial intelligence shows substantial potential to enhance breast cancer imaging, but broader clinical translation requires robust external and prospective validation, improved calibration, assessment of generalizability and bias, and integration into clinical workflows.