# An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
## 1 Introduction  
Viral and bacterial lung diseases are among the most common and dangerous diseases that have affected many people around the world, caused daily death, and spread quickly [1, 2, 3]. Viral and bacterial lung diseases have a very high mortality rate. The World Health Organization projected that 65 million people have chronic obstructive pulmonary disease (COPD) that resulted in 3 million deaths [4]. Around 808,694 children under the age of five died of pneumonia in 2017 [5, 6], and 10 million people had tuberculosis, with 1.4 million deaths [7]. Thus, diagnosing viral and bacterial lung diseases precisely and early to provide the patient with the appropriate treatment is essential. Chest X-rays and computed tomography images are used to diagnose and detect lung diseases. They are considered one of the most efficient diagnostic tools, with over two million procedures performed yearly [8]. However, the diagnostic results are dependent on clinicians' interpretations and experiences. Chest X-ray interpretations of radiologists' disagreement are substantial and reported as high as 56% [9]. The lower resolution of chest X-ray images and the limited experience radiologists have while examining chest X-ray images can cause challenging and diagnostic errors. Moreover, viral and bacterial lung diseases are misdiagnosed because their symptoms and signs overlap, causing severe and critical medical complications that require intensive medical care. Thus, chest X-ray images have been merged with deep learning artificialintelligence techniques in an attempt to minimize human errors and facilitate prompt and more accurate detection and diagnosis of different lung diseases [10; 11; 12; 13; 14; 15; 16; 17; 18]. These artificial intelligence methods, such as K-means, the support vector machine, convolutional neural networks, recurrent neural networks, and generative adversarial networks, are reported to be effective in X-ray image analysis. They are increasingly used to detect lung abnormalities [19; 20; 21]. Deep learning (DL) algorithms have been trained on several widely used chest X-ray image datasets to help clinicians make accurate diagnostic decisions. The Japanese Society of Radio Logical Technology, in cooperation with the Japanese Radiological Society Chest X-ray database (JSRT), is a digital image database containing 154 subjects with lung problems and 93 subjects without lung problems [22; 23]. Montgomery County, MC, USA, has a chest X-ray database for tuberculosis, with 80 healthy subjects and 58 tuberculosis patients [24]. The Guangzhou Women and Children's Medical Center dataset [25] consists of 5863 JPEG-formatted chest X-ray images of children. It has three primary folders: train, test, and validation, each containing subfolders for the normal and pneumonia categories [26]. The COVID-19, pneumonia, and normal chest X-ray posteonterior (PA) dataset [27] has 4575 samples and 1525 images of children and adults diagnosed with COVID-19, pneumonia, or a normal chest [27]. Due to the complex construction of DL algorithms, the training process on these datasets takes a long time. The training time for deep convolutional neural networks on the Montgomery County (MC) chest X-ray database to detect tuberculosis ranges from several hours to a few days [28; 29]. Using a single high-end computational power, the multitask learning model that involves segmentation and classification on the Montgomery County dataset takes approximately 24-48 h. The U-Net for segmentation and DenseNet for classification can extend this time further based on parameter tuning and reiterative enhancements [29; 30]. Moreover, the training time for the U-Net architecture on the JSRT database for lung segmentation ranges from several hours to a few days. Extensive computational resources are required for preprocessing and careful model tuning of 247 high-resolution chest X-ray images to achieve optimal results [30].