ENHANCING PNEUMONIA DETECTION: USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Pneumonia is a dangerous respiratory infection that, particularly in young children and the elderly, can be deadly. Improving patient outcomes requires early diagnosis and treatment. However, access to radiological services is not universally available. Delays in diagnosis and treatment may result from this, which might harm patients.In situations with limited resources, the ability of intelligent systems to automatically identify pneumonia from chest X-rays has the potential to enhance timely diagnosis and medication. With just a tiny quantity of labeled data, in the machine learning technique, we use transfer learning for training intelligent systems. In this study, we suggest a transfer learning-centered intelligent system for early recognition of pneumonia. In a chest X-ray dataset from patients with and without pneumonia, we trained our system.Our results suggest that intelligent systems for pneumonia detection using transfer learning have the potential to improve early diagnosis and treatment in resource-limited settings. We consider that our work has a ubstantial impact on the field of artificial intelligence for healthcare.