# Pneumonia Disease Detection Using Chest X-Rays and Machine Learning
###### Abstract  
Pneumonia is a deadly disease affecting millions worldwide, caused by microorganisms and environmental factors. It leads to lung fluid build-up, making breathing difficult, and is a leading cause of death. Early detection and treatment are crucial for preventing severe outcomes. Chest X-rays are commonly used for diagnoses due to their accessibility and low costs; however, detecting pneumonia through X-rays is challenging. Automated methods are needed, and machine learning can solve complex computer vision problems in medical imaging. This research develops a robust machine learning model for the early detection of pneumonia using chest X-rays, leveraging advanced image processing techniques and deep learning algorithms that accurately identify pneumonia patterns, enabling prompt diagnosis and treatment. The research develops a CNN model from the ground up and a ResNet-50 pretrained model This study uses the RSNA pneumonia detection challenge original dataset comprising 26,684 chest array images collected from unique patients (56% male, 44% females) to build a machine learning model for the early detection of pneumonia. The data are made up of pneumonia (31.6%) and non-pneumonia (68.8%), providing an effective foundation for the model training and evaluation. A reduced size of the dataset was used to examine the impact of data size and both versions were tested with and without the use of augmentation. The models were compared with existing works, the model's effectiveness in detecting pneumonia was compared with one another, and the impact of augmentation and the dataset size on the performance of the models was examined. The overall best accuracy achieved was that of the CNN model from scratch, with no augmentation, an accuracy of 0.79, a precision of 0.76, a recall of 0.73, and an F1 score of 0.74. However, the pretrained model, with lower overall accuracy, was found to be more generalizable. machine learning; CNN; RESNET; pneumonia infections 202518282025182820251820251820251820251820251820251820251820251820251820251820251820251820251820251820251820251820251820251820251820251820225182025182022 expert radiologists whose predictions are required in several areas of the world, according to ref. [6], and, presently, the use of chest X-rays to detect pneumonia can result in delayed detection and treatment; as such, the use of computer-aided detection (CAD) systems can serve as a solution to these problems [7]. Deep learning (DL), a subset of machine learning, is significantly assessed as CAD technology in pneumonia detection [8]. Machine learning is being utilized in medical imaging tasks, mapping, and diagnosing images [9]. Machine learning can solve complex computer vision problems in medical imaging [10]. However, deep learning has taken giant steps in these models because of its ability to learn hierarchical features from raw image data [11]. Deep learning techniques achieve classification such that the key features are spontaneously extracted and classified [12]. Compared to other deep learning techniques, CNN is one of the effective approaches for pattern recognition because of its layering topography [13]. CNNs are a type of deep neural network that concentrates on image analyses and consists of convolutional layers and filters that assist in extracting the spatial and temporal features within an image [14]. CNNs principal benefit is its ability to extract meaningful features from full images [15]. CNN models require large, labeled data, excessive cost, and powerful software. As such, transfer learning as an approach to machine learning solves this issue [16]. Transfer learning is characterized by using a pretrained CNN model to solve another new problem, i.e., using the expertise acquired from it for a particular task, to solve other related tasks [16]. In the next section, the background is presented followed by the methodology section and then results. At the end of this article, we have a discussion section, followed by the conclusion of the work.