COMPARATIVE ANALYSIS OF DEEP LEARNING METHODS FOR PNEUMONIA DETECTION ON X-RAY IMAGES

Abstract

Pneumonia is a potentially fatal bacterial illness that affects one or both lungs in humans and is frequently caused by the bacterium Streptococcus pneumoniae. According to the World Health Organization, pneumonia accounts for one in every three fatalities in India (WHO). Expert radiotherapists must evaluate chest X-rays used to diagnose pneumonia. Thus, establishing an autonomous method for identifying pneumonia would be advantageous for treating the condition as soon as possible, especially in distant places. Convolutional Neural Networks (CNNs) have received a lot of interest for illness categorization due to the effectiveness of deep learning algorithms in evaluating medical imagery. Furthermore, features gained by pre-trained CNN models on large-scale datasets of X-ray pictures are extremely effective in image classification tasks. Several Convolutional Neural Networks were seen to categorize x-ray pictures into two groups, pneumonia and non-pneumonia, using various parameters, hyperparameters, and number of convolutional layers modified by the authors. The study analyzes six different models. The first and second models each include two and three convolutional layers. VGG16, VGG19, ResNet50 and Inception-v3 are the other four pre-trained models.