Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

Abstract

Image processing in medical has been progressing far than it ever did when it's one of the main techniques used in the biomedical imaging system and computer aided diagnosis systems. Few of the well-known medical imaging modalities are Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Scan, X-Ray and Ultrasound. The output from these imaging modalities would later be reviewed by expert for an accurate result. Computer aided diagnosis not only save time as it can process thousand images just in a few minutes, it can also be used as tool to aid the expert as a second opinion, which in turn could save more lives. Thorax consist of several organs which include heart, lungs, thymus gland, muscles and other various internal structures such as diaphragm. Thorax disease refer to diseases that affect the chest and the most common symptom is the chest pain. Some of the common thoracic disease are atelectasis, pneumothorax, pneumonia, nodule, mass, infiltration, effusion and cardiomegaly. Figure 1 shows the thorax diseases observed in chest X-ray [1]. Correct diagnosis and disease treatment are important especially during the early stage, which is the most difficult part of the process due to similarity in appearance.

Deep learning based COVID and Pneumonia detection using chest X-ray

“…Deep learning has given rise to techniques like convolutional neural networks (CNNs). CNN has offered incredible guarantees concerning clinical image investigation [9]. CNNs contain various layers like convolutional layers, and pooling layers, with some number of neurons.…”

“…Chouhan et al [7] proposed a transfer learning model for the prediction of Pneumonia which gave an accuracy of 96.4% and gave a recall value of 99.62% on freshly introduced data. Similarly, deep learning has proved to be an effective way of detecting thoracic diseases [7]- [9], and gastric cancer detection [15], using X-rays. Some of these models use a large dataset to input their models and offer promising results [16].…”

Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

“…AlexNet, VGGNet, GoogleNet and ResNet architecture had used to detect breast cancer using mammogram images [18]. AlexNet architecture alone also used to find the thorax disease in Lung using an X-Ray [19] and a tumour from the breast mammography images [20] and AlexNet, ResNet-101 and InceptionResNet-V2 architecture had used to detect calcification in Cartesian coordinate IVUS images and Polar reconstructed coordinate IVUS images [21].…”