Pneumona Disease Detection using Deep Learning

Abstract

Abstract: Pneumonia is a life-threatening respiratory disease that causes inflammation and fluid accumulation in the lungs. Chest X-ray is one of the most commonly used diagnostic tools to detect pneumonia. In recent years, deep learning techniques have been widely used in the medical field for image analysis and diagnosis. In this study, we propose a deep learning-based method for pneumonia detection using chest X-ray images. This study proposes a Convolutional Neural Network (CNN) based approach for detecting pneumonia using chest X-ray images. The proposed model consists of multiple layers of convolution, pooling, and fully connected layers, designed to extract features from input images and classify them into pneumonia and nonpneumonia categories. The model is trained on a publicly available dataset containing chest X-ray images of patients with and without pneumonia. The performance of the proposed model is evaluated using various metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The results indicate that the proposed model achieved high accuracy, sensitivity, and specificity in detecting pneumonia from chest X-ray images, demonstrating its potential for clinical use in aiding radiologists in diagnosing and monitoring patients with pneumonia, particularly in cases where the diagnosis is challenging due to factors such as image quality or complex cases. Moreover, the proposed approach can be integrated into existing radiology workflows to automate the detection and classification of chest X-ray images, leading to faster and more accurate diagnoses, thereby improving patient outcomes