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...
In the medical field, automated medical image analysis is a key application of deep learning that aids in fast and uniform diagnosis of a disease. In this study, we propose DenseNet121-Attention framework for three class chest X-ray classification: Normal, Bacterial Pneumonia and Viral Pneumonia. The method proposed in...
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...
Lung diseases such as pneumonia and tuberculosis (TB) represent a major global health burden, particularly in low- and middle-income regions with limited access to expert radiological interpretation. Early and accurate diagnosis through chest X-ray (CXR) imaging is critical, yet conventional radiological interpretation...
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...
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