# Classification and Predictions of Lung Diseases from Chest X-rays Using MobileNet V2
## 1 Introduction  
Different types of lung disease have affected many people around the world. Lung diseases make the lungs more prone to certain physical problems and air pollution. As a result, lung function is impaired. Some lung diseases, such as emphysema, asthma, pleural effusion, tuberculosis, and other diseases including aspiration fibrosis, pneumonia, and lung malignancies, lead to loss of versatility in the lungs, which causes a decrease in the total volume of air [1]. Lung disease spreads easily, so it is important to identify the problem and provide the patient with the appropriate treatment. To detect and diagnose lung diseases, radiologists mainly deal with chest X-rays. Radiologists can use chest X-rays to diagnose and detect diseases and many other conditions. These diseases include bronchitis, infiltrations, atelectasis, pericardiitis, fractures, and many others [2]. Chest radiography is considered the most-used type of disease examination in the world, with more than 2 million procedures performed each year [3]. This technology is essential and critical for the examination, diagnosis, and treatment of chest diseases, which is one of the main causes of death worldwide [4]. Therefore, to improve workflow priority and clinical decision support in large-scale projections and global population health programs, computer systems must be used to interpret chest radiographs as effectively as radiologists. In practice, this could play an importantrole in many clinical environments. There is a lot of work done using AI technology to detect and diagnose lung diseases [5; 6]. As a kind of probabilistic neural network, multilayer perceptron, learning vector quantization [7], and recurrent neural network (RNN) have been used to diagnose lung disease [8]. For the detection of lung diseases such as tuberculosis [PERSON], [PERSON] et al. [9] proposed a new hybrid method for tuberculosis X-ray image classification by extracting features from X-ray images with transfer learning and then filtering the produced huge number of features using the recently proposed Artificial Ecosystem-based Optimization (AEO); this method surpassed 90% accuracy in two datasets. Chronic Obstructive Pulmonary Disease (COPD) and pneumonia were performed with a neural network to diagnose cancer [10]. [PERSON] et al. [11] exceeded the state-of-the-art in lung tumor segmentation by proposing parallel deep learning model a hybrid attention mechanism and DenseNet, which got 94% accuracy. The advances in deep learning and large datasets allow the algorithm to match the performance of medical professionals in various medical imaging tasks, including the detection of diabetic retinopathy [12], the classification of skin cancer [13], and metastases in the detection of lymph nodes [14]. The automatic diagnosis of chest imaging has attracted increasing attention [15; 16] and special algorithms have been developed for the detection of pulmonary nodules [17], but to detect other diseases, such as pneumonia and pneumothorax, a method to classify multiple pathologies at the same time is suggested. [PERSON], [PERSON] et al. [18] developed a new method to detect pneumonia by computing the handcrafted features from the chest X-ray with the use of a modified VGGNet19 (Visual Geometry Group Network) [19]. This method achieved a 97.94% classification accuracy (Check Table 1). Until recently, computing power and the availability of large datasets allowed this approach to flourish. The National Institute of Health published ChestX-ray14 [20], which has led to many studies using deep learning for diagnosis from chest X-rays [21; 22; 23]. Convolutional neural networks are the most widely used methods from ImageNet [25]. We want to deliver high accuracy results while keeping the parameters and mathematical operations as low as possible to bring deep neural networks to mobile devices.