# A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images
## 1 Introduction  
Today's deep learning models can reach human-level accuracy in analyzing and segmenting an image [1]. The medical industry is one of the most prominent industries, where deep learning can play a significant role, especially when it comes to imaging. All those advancements in deep learning make it a prominent part of the medical industry. Deep learning can be used in wide variety of areas like the detection of tumors and lesions in medical images [2; 3], computer-aided diagnostics [4; 5], the analysis of electronic health-related data [6], the planning of treatment and drug intake [7], environment recognition [8] and brain-computer interface [9], aiming to come up with decision support for the evaluation of the person's health. The key element of the success of deep learning is based on the capability of the neural networks to learn high level abstractions from input raw data through a general purpose learning procedure [10]. Although currently, deep learning still cannot replace doctors/clinicians in medical diagnosis, it can provide support for experts in the medical domain in performing time-consuming works, such as examining chest radiographs for the signs of pneumonia. Pneumonia is an inflammation of the lungs that may be caused by pathogens, like bacteria, viruses and fungi [11]. It can occur to anyone, young or even to healthy people. It becomes life-threatening for infants, people having other diseases, people with an impaired immune system, elderly people, people who are hospitalized and have been placed on a ventilator, people with chronic disease like asthma and people who smoke cigarettes. The cause of pneumonia also determines is severity. Viral pneumonia is milder, and symptoms occur gradually. However, it can become complicated to diagnose if a bacterial infection develops at same time with viral pneumonia. On the other side, bacterial pneumonia is more severe, and its symptoms can occur gradually or even suddenly, especially among children [12]. This type of pneumonia affects a large part of the lungs, and can lead to affect many lobes of the lung. When multiple lobes of the lungs are affected, a person needs to be hospitalized [13]. Another form of pneumonia is fungal pneumonia, which can occur to persons with weak immune systems. This type of pneumonia could be dangerous also, and requires time for the patient to regain health. Therefore, there is an urgent need to perform research and to develop new methods helping to provide computer-aided diagnosis to reduce pneumonia-related mortality, especially, child mortality, in the developing world [14]. The analysis of chest radiography has a crucial role in medical diagnostics and the treatment of the disease. The Centers for Disease Control and Prevention (CDC, Atlanta, GA, USA) reported that about 1.7 million adults in the US seek care in a hospital due to pneumonia every year, and about 50,000 people died in United States from pneumonia in 2015 [15]. Chronic obstructive pulmonary disease (COPD) is the primary cause of mortality in the United States, and it is projected to increase by 2020 [16]. The World Health Organization (WHO, Geneva, CH) reported that it is one of the leading causes of death all over the world for children under 5 years of age, killing an estimated 1.4 million, which is about 18% of all children deaths under the age of 5 years worldwide [17]. More than 90% of new diagnoses of children with pneumonia happen in the underdeveloped countries with few medical resources available. Therefore, the development of cheap and accurate pneumonia diagnostics is required. Recently, a number of researchers have proposed different artificial intelligence (AI)-based solutions for different medical problems. Convolutional neural networks (CNNs) have allowed researchers to obtain successful results in wide medical problems like breast cancer detection, brain tumor detection and segmentation, disease classification in X-ray images, etc. [18]. For example, [PERSON] et al.