Advanced Predictive Modeling for Pneumonia Diagnosis to Revolutionize Healthcare with Transfer Learning

A timely diagnosis is essential for the proper treatment of pneumonia, a serious respiratory infection. Machine Learning techniques offer the potential to improve pneumonia prediction by analysing medical imaging data (chest X-ray images) and patient information. This research explores the application of Machine Learning, specifically transfer learning (TL) using the ResNet50 architecture, to enhance healthcare outcomes through predictive modelling for disease diagnosis, with a focus on pneumonia. Leveraging a pre-trained ResNet50 Convolutional Neural Network (CNN) model, this research implemented transfer learning (TL) techniques to fine-tune the network at required level for the specific task of pneumonia detection from chest X-ray images. The proposed approach using ResNet50 model, achieved a remarkable accuracy (98.47%), F1 score (97.2%), and Precision (99.65%). This high level of accuracy demonstrates the capacity of cutting-edge machine learning techniques using the proposed algorithm to significantly improve diagnostic processes in healthcare, offering a promising tool for early and accurate pneumonia diagnosis, which is critical for timely and effective treatment. This research underscores the efficacy of transfer learning in medical imaging and pave the way for its broader application in various domains of disease diagnosis.