Year: 2025
Type: article
Abstract: Pneumonia is a major global health challenge requiring timely and accurate diagnosis to enhance patient outcomes. This study introduces a Convolutional Neural Network (CNN)-based model trained on labeled chest X-ray datasets to classify images as either pneumonia-positive or normal. By employing advanced techniques such as transfer learning and fine-tuning of pre-trained models, the system achieves robust feature extraction, ensuring exceptional diagnostic accuracy. Preprocessing steps, including image normalization, data augmentation, and contrast enhancement, are applied to improve the model’s performance and generalizability across diverse datasets. The evaluation metrics, including accuracy, precision, recall, and F1-score, affirm the model’s reliability, often matching or surpassing the diagnostic efficiency of human radiologists. This AI-driven diagnostic tool offers transformative potential for healthcare by reducing diagnostic time, assisting medical professionals, and addressing expertise shortages in resource-constrained areas. Future objectives include extending the model’s capabilities to detect multiple lung diseases and implementing it in clinical environments for continuous assessment and improvement. Index Terms - Pneumonia Detection, Convolutional Neural Network, Chest X-rays, Transfer Learning, Image Preprocessing, Deep Learning, Diagnostic Tool, Healthcare AI, Data Augmentation, Lung Disease Detection.
DOI: 10.55041/ijsrem44461
Authors: G. Bharadhwaj, G. Shivaji
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4409435852