PNEUVISION: DEEP LEARNING FOR PNEUMONIA DIAGNOSIS

Abstract

Early identification is crucial for improved results in pneumonia, which is described by inflammation of the lungs. The principle objective of this investigation is to design and implement an advanced deep learning scheme for the purpose of detecting pneumonia. Development of a system incorporating a diverse array of chest X-ray images depicting both healthy and pneumonia- stricken patients is the aim of this research. Models being trained to recognize certain attributes that indicate the presence of pneumonia. Architectures like CNN, ResNet-50, DenseNet-121, and InceptionNet V3 are capable of extracting hierarchical features. Through extensive training on a substantial dataset, the parameters of the model are refined, leading to enhanced capability in distinguishing between individuals with normal conditions and those with pneumonia. The effectiveness of the model in identifying pneumonia is assessed through the utilization of metrics including accuracy, sensitivity, and specificity. The efficacy of deep learning models is substantiated by empirical evidence, which underscores their capability to develop robust pneumonia detection systems that enable prompt medical intervention.