# An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
## 1 Introduction
Training the ResNet-34 model on the Guangzhou Women and Children's Medical Center dataset for around 100 epochs on a high-end graphics processing unit can take approximately 10 to 20 h [31]. On the other hand, training GoogLeNet, ResNet-18, and DenseNet 121 models collectively on the COVID-19, pneumonia, and normal chest X-ray PA dataset [27] with resizing images to smaller dimensions took several hours to a few days [32]. Another study emphasized that transfer learning employing VGG-19 and ResNet-50 models demonstrated high accuracy in detecting pneumonia. However, training times on the COVID-19, pneumonia, and normal chest X-ray PA datasets [27] extend to a day or more in a powerful Graphics processing unit [33]. Such computational resources and advanced technology are unavailable in remote hospitals and underdeveloped countries. Thus, simplifying this technology and making it available will promote human well-being. The model training time can vary significantly based on the complexity of the model, the computational resources available, the model architecture, the dataset size, the number of epochs, batch size, and the computational power. The deep-learning algorithms consist of multiple layers that integrate alternating convolutional and pooling processing layers for feature augmentation, enhancement of the image resolution, and extraction, followed by one or more fully connected layers for category classification [34; 35; 36]. These deep learning algorithms showed promising results in dealing with 2D medical images, spectral images, and video frames [34; 35; 36; 37; 38]. However, they require high computational complexity levels, where it takes a long time to analyze 2D medical images and an extensive training dataset for training the classifier to minimize overfitting problems. Based on the available literature, no study has converted a 2D chest X-ray into 1D data to train deep-learning algorithms to detect lung abnormalities but has not detected a cardiomegaly-level (enlarged heart). Previously, 2D chest X-rays were transformed to 1D to detect a cardiomegaly-level (enlarged heart) rapidly. It showed high accuracy, reaching 98.00% higher than the traditional 2D approach. However, the image transformation method used all the image pixels instead of utilizing a statistical representation to reduce image dimensions and carry meaningful information. Using statistical methods for feature extraction provides a more straightforward approach with more insights into the image. Moreover, it used pooling for downsampling to simplify data by taking maximum values, which may result in a loss of fine details [39]. Therefore, simplifying the complexity of image processing and classification is essential. Figure 1 illustrates our proposed method for achieving this by transforming 2D images from the Guangzhou Women and Children's Medical Center dataset [25] and the COVID-19, pneumonia, and a normal chest X-ray PA dataset [27] into one-dimensional texture representation (1 DTR) data. Then, the 1D data will train a deep learning classifier to classify lung diseases. Our contribution involves converting the 2D chest X-ray images into 1 DTR, proposing an efficient, quick, and cost-effective method to classify lung diseases. Instead of extracting the features using moving windows or pooling, the texture representations consider a vertical extraction and histogram for dimensional reduction and distinctive data representation. This method could be suitable for remote clinics and emergency rooms for fast diagnosis in low-resource environments.