Deep Learning-Enabled Healthcare Management System for Early Pneumonia Detection

Abstract

Modern healthcare systems often struggle with fragmented tools and manual processes, which can hinder timely diagnosis and efficient patient management. This fragmentation creates a critical need for a unified platform that integrates advanced diagnostics with administrative workflows. This study presents the design and implementation of a deep learning–enabled healthcare management system that combines four core modules: AI-driven symptom analysis, early disease detection with a convolutional neural network (CNN) focused on pneumonia diagnosis, secure electronic health record (EHR) management, and intelligent appointment scheduling. The CNN model was evaluated on 624 labelled chest X-ray images (390 pneumonia, 234 normal) using publicly available clinical datasets. The model achieved an accuracy of 87.98%, area under the ROC curve (AUC) of 0.9596, precision of 0.9462, and recall of 0.8564, indicating strong performance for early pneumonia detection. Simulated patient records were used to assess system-level performance, with the scheduling engine reducing average waiting times by approximately 30% compared to manual processes. The integration of deep learning with healthcare management and teleconsultation services demonstrates measurable improvements in diagnosis accuracy, data security, and workflow efficiency. The proposed system thus provides a practical and scalable framework for modern healthcare delivery.