Abstract: Pneumonia is still one of the key public health problems, particularly in resource-poor areas like Kisii County in Kenya, where poor diagnostic equipment hinders early diagnosis. In this project, the intention was to develop and evaluate an explainable deep learning approach for the diagnosis of pneumonia usi...
Results showed that the proposed DenseNet121-Attention framework is effective in efficiently and interpretively classifying the chest X-rays and can be used to support computer-aided diagnosis (CAD).
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...
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.
One of the most popular and less costly tools of diagnosing lung related diseases is the chest X-rays. Nonetheless, the manual analysis is time consuming and it is subject to error particularly in resource constrained environments. Most of the existing models only deal with pneumonia or they deal with complex feature e...
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...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
