# PneumoNet: Artificial Intelligence Assistance for Pneumonia Detection on X-Rays
## 2 Background and Related Work  
Pneumonia is an infection that inflames the air sacs in one or both lungs. These air sacs, called alveoli, can fill with fluid or pus, causing symptoms such as cough with phlegm or pus, fever, chills, and difficulty breathing [11]. Pneumonia can affect people of all ages but is often more severe in infants, young children, the elderly, and those with underlying health conditions or compromised immune systems. Diagnosis involves a combination of physical exams, medical history, imaging tests (such as X-rays or CT scans), blood tests, and sometimes sputum cultures to identify the specific cause of infection [12; 13]. Let's begin by concentrating on the datasets. In the context of pneumonia diagnosis, a collection of medical images, namely X-rays, is typically utilised to train and evaluate the models' performance; these images can show both symptoms and no symptoms. Three different datasets will be used in the following research; one of them, Dataset #1, is the chest X-ray (COVID-19 and pneumonia) [8], which contains 3418 X-rays with pneumonia and 1266 normal for training purposes. Dataset #2, optical coherence tomography and chest X-ray images for classification dataset [9], composed of optical coherence tomography (OCT) images, is divided into training and testing sets, each containing images from distinct patients (randomised patient ID and image number by this patient). The training set consists of 3883 X-rays corresponding to cases of pneumonia and 1349 X-rays without detected pathologies, and a test set with 234 images labelled as pneumonia and 390 without detected pathologies. Dataset #3 is the chest X-ray images (pneumonia) dataset [10]. In the training dataset, there are 1341 images as normal and 3875 X-rays with pneumonia; including validation and test, there are a total of 5856. The dataset comprises three main folders (train, test, validation) with subfolders for each image category (Pneumonia/Normal), totalling 5863 JPEG X-ray images across two categories. These chest X-ray images (anterior-posterior) were sourced from paediatric patients aged one to five at Guangzhou Women and Children's Medical Centre as part of routine clinical care. Table 1 summarises the characteristics of the three public datasets used in the following research, mentioning the number of X-rays with and without pneumonia for training, testing, and validation. The radiological standards that have guided research for years and our long-held beliefs about pneumonia are being called into question by recent researchers. A systematic evaluation of the literature regarding the classification of pneumonia X-ray images has not been conducted here; for that, see [14; 15; 16; 17; 18]. Here, the paper concentrates on studies that support the validity of the research model. The work of [PERSON] and [PERSON] [19] shows a DL model employing VGG16 to detect and categorise pneumonia using chest X-ray images, achieving an accuracy of 92.15%, a recall of 0.9308, a precision of 0.9428, and an F1 score of 0.937 for one of the datasets, the chest X-ray (Dataset #1) [8]. Additionally, in another \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **X-Ray Normal** & **X-Ray Pneumonia** \\ \hline Dataset \#1 [8] & Train: 1266 & Train: 3418 \\ Chest X-ray (COVID-19 and Pneumonia) & Test: 317 & Test: 855 \\ Dataset \#2 [9] & Train: 1349 & Train: 3883 \\ Optical Coherence Tomography and Chest X-ray & Test: 234 & Test: 390 \\ (Dataset \#3) [10] & Train: 1341 & Train: 3875 \\ Chest X-ray Images (Pneumonia) & Val: 8; Test: 234 & Val: 8; Test: 390 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The datasets used in the research (described in the text). CXR dataset [10], the mentioned model achieved an accuracy of 95.40%, a recall of 0.954, a precision of 0.954, and an F1 score of 0.954.