# A Deep Learning Review of ResNet Architecture for Lung Disease Identification in CXR Image
## 1 Introduction  
Two of the most crucial organs in the human body, the lungs, have a direct impact on both the respiratory and circulatory systems. Injury to the human lungs has a significant impact on the continuity of all bodily functions. Several lung diseases, such as COVID-19, pneumonia, and other diseases, result in a great number of fatalities. The majority of diseases that affect the lungs are brought on by bacteria, viruses, fungi, chemical exposure, lung injury, or even the indirect effects of other diseases. Streptococcus bacteria, Mycoplasma bacteria, adenovirus, rhinovirus, influenza virus, respiratory syncytial virus (RSV), and parafinfluenza virus are the pathogens that cause pneumonia [1]. The World Health Organization (WHO) describes pneumonia as a lung-specific acute respiratory disease. Pneumonia is a contagious disease that can affect both children and adults. The most common cause of infection-related death in children worldwide is pneumonia. A total of 4,685,164 individuals under the age of 15 and 9,564,023 individuals over the age of 15 died from pneumonia in 2019. By 2030, there will be about 11 million child deaths worldwide due to pneumonia, according to a study by Johns Hopkins University and Save the Children. The COVID-2019 disease is a respiratory infection brought on by the SARS-CoV-2 virus [2]. The virus spreads through the air from an infected person's lipsor nose when they cough, sneeze, sing, talk, or breathe. Globally, COVID-19 is spreading. Overall, 6,456,988 people died as a result of COVID-19 in December 2019. Lung opacities are an indication of abnormalities in a person's lungs other than cancer [3]. This might be an instance of lung bleeding, infection, or experience of other unidentified issues. Numerous factors with varying degrees of significance can contribute to lung opacities. Viral infections and other lung clouding diseases frequently have short-term effects with little long-term harm. Other conditions, like lung cancer and alveolar hemorrhage, call for more serious treatment. There are several lung diseases that share the same symptoms but require different treatments, such as COVID-19 and pneumonia [4]. Determining the presence of infection in the early stages of the disease is one of the most challenging aspects of COVID-19 [5]. It is difficult to find similarities between people who have lung-related diseases in terms of their initial personality traits. A crucial first step is the accurate diagnosis of the disease. With the rapid development of technology, several fields, including the medical field, began to use Artificial Intelligence (AI) to support ongoing work processes. Medical imaging is one element that is frequently used in the application of technology in the medical field. Medical images are used to diagnose or detect disease based on image or video data [6]. Image data can be obtained using a variety of techniques, such as magnetic resonance imaging (MRI), X-rays, ultrasonography (USG), endoscopy, computerized tomography (CT) scans, and nuclear medicine [7]. CT scan data are usually used to segment various diseases, such as lung disease. However, segmenting lung nodules on CT images still needs the expertise and experience of a medical professional, specifically a radiologist, and is performed manually by depicting the nodule one slice at a time. This procedure must be repeated several times and is subject to subjectivity, which can lead to misdiagnosis. As a result, accurate automatic lung nodule segmentation has always been at the forefront of computer-assisted diagnosis research [8]. As a result, many studies are competing to achieve good evaluation values for this case, so that the specificity and sensitivity for CT scans are 94% and 37%, respectively. X-ray radiography is frequently the preferred option in clinical settings due to its benefits, including its lower cost and availability in many hospitals, even though CT scans offer higher sensitivity for detection.