2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS), 8-9 March, Dhaka, Bangladesh Pneumonia Detection from Chest X-ray Images using Convolutional Neural Network Md. Zakir Hossain Department of CSE Green University of Bangladesh Dhaka, Bangladesh Email: mdzakir2551@gmail.com Khalid Ibne Mostafa Department of CSE Green University of Bangladesh Dhaka, Bangladesh Email: khaledmd26937@gmail.com Md. Mostafizur Rahman Department of CSE Green University of Bangladesh Dhaka, Bangladesh Email: mostafizurrahman0202@gmail.com Md. Solaiman Mia Department of CSE Green University of Bangladesh Dhaka, Bangladesh Email: solaiman@cse.green.edu.bd Abstract—The fact that Pneumonia ranks among the world’s most common causes of mortality, precise diagnosis methods are absolutely necessary. Although chest X-rays are a quick way to identify Pneumonia, they can be challenging to interpret due to the similarities between Pneumonia and other lung conditions. This research presents a computer-aided approach to Pneumonia diagnosis that uses chest x-rays to enhance diagnostic decision-making. Moreover, situations such as the coronavirus pandemic, where widespread lockdowns are implemented and human contact poses significant risks, highlight the importance of computer-aided diagnosis like this. Consequently, a technique for the quick and automatic diagnosis of Pneumonia is presented in this work. Based on chest X-ray pictures, a deep learning- based architecture called “ESPD” is suggested for the automatic diagnosis of Pneumonia. A benchmark dataset comprising 5,856 chest X-ray pictures was utilized for the suggested deep-learning network’s testing, evaluation, and training. The total accuracy of the suggested model was found to be 98.24%, consisting of 0.98 F1-Score, 0.98 precision, 0.98 recall, and 0.97 specificity. In comparison to previous methods in the literature, the suggested method was found to be quicker and less computationally expensive, and it also produced accuracy that showed promise. Keywords—Pneumonia, Chest X-ray, Data Augmentation, Con- volutional Neural Network. I. I NTRODUCTION Chronic infections of the lungs like Pneumonia affect an excessive number of young children around the globe every year. Pneumonia was the primary reason for among children under the tender age of five between 1990 and 2019 [1]. In areas with poor healthcare services, the most common cause of death for children and young adults is Pneumonia. The lack of a reliable healthcare system in many developing and middle-income countries compounds the problem. The elderly and those with prior health risks are particularly at risk from this condition. Rapid breathing, discomfort in the chest area, prolonged coughing, initial cold-like symptoms and difficulties maintaining normal breathing patterns are all signs of Pneumonia, as defined by the World Health Organization (WHO) [2]. Sustainability integrates the utilization of Convolutional Neural Network (CNN) for identifying cases of Pneumonia, combining both environmentally conscious procedures with medical evaluation [3]. The utilization of CNNs for imaging purposes boosts Pneumonia determination, resulting in rapid treatment. Industry 5.0 ensures environmentally friendly oper- ations and environmentally compatible hardware options. This collaboration serves equally leading-edge medical services and sustainable practices research and development initiatives [3]. The laboratory preferred method for Pneumonia is ra- diography (chest X-rays). Notwithstanding this, psychiatrists continue analyzing images individually, in spite of being constrained due to variables like exhaustion and their lack of experience. It necessitates quite a bit of effort and financial resources to turn into an appropriately trained radiology [4]. The rural parts of a nation with low revenues additionally experience a lack of radiologists. With the resemblance to other illnesses, evaluating Pneumonia via an X-ray of the chest may prove challenging even to highly qualified radiologists. If a doctor gives the inaccurate diagnosis, for instance, the individual in question might miss the medical treatment they require and can die as the consequence. Thus, Pneumonia could be problematic to identify properly [5]. For all of these factors, developing of Computer Aided Diagnosis (CAD) to assist in determining the presence of Pneumonia is of the greatest possible significance [6]. In recent years, CAD methods based on Deep Learning (DL) are gaining acceptance for use in the field of medicine. CNN, a type of DL-based CAD system inspired by the visual cortex of people, are able to learn from a wide range of information, particularly data that has been appraised by psychiatrists [7]. This paper introduces a deep learning based architecture called ESPD (Early Stage Pneumonia Detection) specifically designed for the automatic diagnosis of Pneumonia from chest X-ray images. Our proposed model achieves a high accuracy of 98.24% on a benchmark dataset consisting of 5,856 chest X-ray images. 979-8-3503-5028-9/24/$31.00 © 2024 IEEE