PNEUMONIA DETECTION THROUGH CNN AND RESNET-50

Summary: Pneumonia is a prevalent illness that has a global impact, primarily affecting children and older individuals. Timely identification is essential for immediate intervention, particularly in regions with restricted healthcare availability. The purpose of this paper is to compare how well two methods work for finding pneumonia on chest X-rays creating a custom Convolutional Neural Network (CNN) and using a model that has been pre-trained, such as ResNet-50. The results indicated that although the customized CNN had difficulties achieving satisfactory performance, the ResNet-50 model demonstrated encouraging outcomes following the process of fine-tuning. This paper seeks to improve the detection of pneumonia, especially in disadvantaged places with limited medical resources, by utilizing modern technologies such as deep learning and pre-trained models. Considering the results gained to enhance patient outcomes and decrease mortality rates associated with pneumonia by enabling more precise and prompt detection, this has a beneficial effect on global public health