Lung Infection Segmentation And Classification Using Deep Learning

Abstract

Lung Fibrosis Is A Chronic And Progressive Respiratory Disease Characterized By Scarring Of Lung Tissue, Leading To Reduced Lung Function And, In Severe Cases, Respiratory Failure. Early Detection Is Critical For Timely Intervention And Improved Patient Outcomes. Traditional Diagnostic Methods Rely Heavily On Manual Interpretation Of Imaging Data And Clinical Records, Often Causing Delays Due To Subtle Early-stage Manifestations. This Project Proposes An AI-enhanced Early Lung Fibrosis Detection Platform That Integrates Medical Imaging And Patient Clinical Records To Improve Diagnostic Accuracy And Efficiency. Using A Multimodal Data Fusion Approach, The System Combines Imaging Features With Electronic Health Record Insights To Classify Patients Into Categories Such As Normal, Early-stage Lung Fibrosis, And Advanced Fibrosis. By Providing Intelligent Decision Support, The Platform Assists Clinicians In Early Diagnosis, Reduces Ambiguity, And Promotes Faster Treatment Planning.

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