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

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Tengku Afiah Mardhiah Tengku Zainul Akmal ◽  
Joel Chia Ming Than ◽  
Haslailee Abdullah ◽  
Norliza Mohd Noor
Keyword(s):  
Image Classification ◽  
X Ray ◽  
Chest X Ray

scholarly journals XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Vishu Madaan ◽  
Aditya Roy ◽  
Charu Gupta ◽  
Prateek Agrawal ◽  
Anand Sharma ◽  
...  
Keyword(s):  
Image Classification ◽  
Second Phase ◽  
Two Phase ◽  
X Ray ◽  
The Neural Network ◽  
Rapid Spread ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Two Phases ◽  
Active Patients

AbstractCOVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

scholarly journals Two-stream collaborative network for multi-label chest X-ray Image classification with lung segmentation

2020 ◽  
Vol 135 ◽  
pp. 221-227 ◽  
Author(s):  
Bingzhi Chen ◽  
Zheng Zhang ◽  
Jianyong Lin ◽  
Yi Chen ◽  
Guangming Lu
Keyword(s):  
Image Classification ◽  
Collaborative Network ◽  
Lung Segmentation ◽  
X Ray ◽  
Chest X Ray

scholarly journals FractalCovNet architecture for COVID-19 Chest X-Ray image Classification and CT-scan image Segmentation

2021 ◽  
Author(s):  
Hemalatha Munusamy ◽  
Karthikeyan JM ◽  
Shriram G ◽  
Thanga Revathi S ◽  
Aravindkumar S
Keyword(s):  
Image Segmentation ◽  
Ct Scan ◽  
Image Classification ◽  
X Ray ◽  
Chest X Ray

scholarly journals MS-ANet: deep learning for automated multi-label thoracic disease detection and classification

2021 ◽  
Vol 7 ◽  
pp. e541
Author(s):  
Jing Xu ◽  
Hui Li ◽  
Xiu Li
Keyword(s):  
Image Classification ◽  
Response Characteristic ◽  
Disease Classification ◽  
Practical Significance ◽  
X Ray ◽  
Multi Scale ◽  
Main Challenge ◽  
Chest Disease ◽  
Chest X Ray ◽  
Different Response

The chest X-ray is one of the most common radiological examination types for the diagnosis of chest diseases. Nowadays, the automatic classification technology of radiological images has been widely used in clinical diagnosis and treatment plans. However, each disease has its own different response characteristic receptive field region, which is the main challenge for chest disease classification tasks. Besides, the imbalance of sample data categories further increases the difficulty of tasks. To solve these problems, we propose a new multi-label chest disease image classification scheme based on a multi-scale attention network. In this scheme, multi-scale information is iteratively fused to focus on regions with a high probability of disease, to effectively mine more meaningful information from data. A novel loss function is also designed to improve the rationality of visual perception and multi-label image classification, which forces the consistency of attention regions before and after image transformation. A comprehensive experiment was carried out on the Chest X-Ray14 and CheXpert datasets, separately containing over 100,000 frontal-view and 200,000 front and side view X-ray images with 14 diseases. The AUROC is 0.850 and 0.815 respectively on the two data sets, which achieve the state-of-the-art results, verified the effectiveness of this method in chest X-ray image classification. This study has important practical significance for using AI algorithms to assist radiologists in improving work efficiency and diagnostic accuracy.

scholarly journals 4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection

2020 ◽  
Author(s):  
Asmaa Abbas ◽  
Mohammed M. Abdelsamea ◽  
Mohamed Medhat Gaber
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Large Scale ◽  
Learning Strategy ◽  
Classification Model ◽  
Decomposition Approach ◽  
X Ray ◽  
Sample Decomposition ◽  
Chest X Ray ◽  
Coarse To Fine

ABSTRACTDue to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this paper, we propose a novel deep convolutional neural network, we called Self Supervised Super Sample Decomposition for Transfer learning (4S-DT) model. 4S-DT encourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabelled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a class-decomposition layer to simplify the local structure of the data. 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream class-decomposition mechanism. We used 50,000 unlabelled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. 4S-DT has achieved an accuracy of 97.54% in the detection of COVID-19 cases on an extended test set enriched by augmented images, out of which all real COVID-19 cases were detected, which was the highest accuracy obtained when compared to other methods.

CHEST X-RAY IMAGE CLASSIFICATION USING FASTER R-CNN

2019 ◽  
Vol 4 (1) ◽  
pp. 225
Author(s):  
Azlan Ismail ◽  
Taufik Rahmat ◽  
Sharifah Aliman
Keyword(s):  
Image Classification ◽  
Computation Time ◽  
Classification Model ◽  
High Confidence ◽  
X Ray ◽  
Applied Model ◽  
Reading Errors ◽  
Normal Chest ◽  
Chest X Ray ◽  
The Common

Chest x-ray image analysis is the common medical imaging exam needed to assess different pathologies. Having an automated solution for the analysis can contribute to minimizing the workloads, improve efficiency and reduce the potential of reading errors. Many methods have been proposed to address chest x-ray image classification and detection. However, the application of regional-based convolutional neural networks (CNN) is currently limited. Thus, we propose an approach to classify chest x-ray images into either one of two categories, pathological or normal based on Faster Regional-CNN model. This model utilizes Region Proposal Network (RPN) to generate region proposals and perform image classification. By applying this model, we can potentially achieve two key goals, high confidence in the classification and reducing the computation time. The results show the applied model achieved higher accuracy as compared to the medical representatives on the random chest x-ray images. The classification model is also reasonably effective in classifying between finding and normal chest x-ray image captured through a live webcam.

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