Accurate Prediction of Classifcation Score using DenseNet for Acute Pneumonia

Pneumonia, an acute lung infection, presents varying severity levels, necessitating swift and accurate assessment for timely intervention and improved patient outcomes. While chest X-rays conventionally serve as the primary diagnostic tool, manual interpretation introduces subjectivity and time constraints. This study advocates harnessing deep learning, specifically the DenseNet-121 architecture, to automate pneumonia severity assessment in chest X-ray images. This approach marks a substantial leap toward more effective and cost-efficient health-care practices, leveraging the power of advanced technologies to address the challenges posed by pneumonia diagnosis and severity evaluation. The integration of the proposed deep learning model stands as a promising avenue for improving both diagnostic accuracy and resource optimization in the context of pneumonia management. Pneumonia is a leading cause of mortality world-wide, particularly in developing countries. Accurate and timely detection can help reduce mortality rates by enabling healthcare professionals to intervene before the condition becomes severe. In clinical practice, the ability to detect pneumonia in real-time is crucial for timely intervention. Research is needed to develop efficient models and algorithms that can provide rapid and accurate results suitable for deployment in healthcare settings.