The Classification and Segmentation of Pneumonia using Deep Learning Algorithms: A Comparative Study

Abstract

The paper uses convolutional neural networks (CNNs) to analyze radiography pictures and discriminate between areas afflicted by pneumonia and normal lung tissue.  A sizable dataset of annotated chest X-rays is used to train the deep learning model, which enables it to pick up on complex patterns and characteristics linked to pneumonia. Pneumonia is a significant respiratory disease that affects a large number of individuals worldwide. Timely and accurate diagnosis of pneumonia plays a crucial role in effective treatment and management of the disease. We evaluate the performance of several up-to-date convolution neural network (CNN) architectures, namely ResNet-50, VGG-16, and DenseNet-121, then compare their results with traditional machine learning classifiers.  Recent advancements in deep learning methods have shown accurate results in the investigation and diagnosis of medical image data, including the detection of pneumonia. This paper examines different deep-learning methods for categorizing pneumonia from lung X-ray imagery. Our results show that deep learning techniques performed better than conventional machine learning techniques in classifying pneumonia, with an estimated accuracy of 95% across all of the examined CNN models. These results demonstrate the potential of deep learning algorithms to significantly improve the accuracy and effectiveness of pneumonia diagnosis, supporting physicians in making knowledgeable decisions about patient care.