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 COVID-19 Chest X-Ray Image Classification Using Deep Learning

2021 ◽  
Author(s):  
Soumava Dey ◽  
Gunther Correia Bacellar ◽  
Mallikarjuna Basappa Chandrappa ◽  
Raj Kulkarni
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Image Classification ◽  
Web Application ◽  
Human Error ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
X Ray ◽  
Chest X Ray ◽  
User Friendly

The rise of the coronavirus disease 2019 (COVID-19) pandemic has made it necessary to improve existing medical screening and clinical management of this disease. While COVID-19 patients are known to exhibit a variety of symptoms, the major symptoms include fever, cough, and fatigue. Since these symptoms also appear in pneumonia patients, this creates complications in COVID-19 detection especially during the flu season. Early studies identified abnormalities in chest X-ray images of COVID-19 infected patients that could be beneficial for disease diagnosis. Therefore, chest X-ray image-based disease classification has emerged as an alternative to aid medical diagnosis. However, manual detection of COVID-19 from a set of chest X-ray images comprising both COVID-19 and pneumonia cases is cumbersome and prone to human error. Thus, artificial intelligence techniques powered by deep learning algorithms, which learn from radiography images and predict presence of COVID-19 have potential to enhance current diagnosis process. Towards this purpose, here we implemented a set of deep learning pre-trained models such as ResNet, VGG, Inception and EfficientNet in conjunction with developing a computer vision AI system based on our own convolutional neural network (CNN) model: Deep Learning in Healthcare (DLH)-COVID. All these CNN models cater to image classification exercise. We used publicly available resources of 6,432 images and further strengthened our model by tuning hyperparameters to provide better generalization during the model validation phase. Our final DLH-COVID model yielded the highest accuracy of 96% in detection of COVID-19 from chest X-ray images when compared to images of both pneumonia-affected and healthy individuals. Given the practicality of acquiring chest X-ray images by patients, we also developed a web application (link: https://toad.li/xray) based on our model to directly enable users to upload chest X-ray images and detect the presence of COVID-19 within a few seconds. Taken together, here we introduce a state-of-the-art artificial intelligence-based system for efficient COVID-19 detection and a user-friendly application that has the capacity to become a rapid COVID-19 diagnosis method in the near future.

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 Automated segmentation and diagnosis of pneumothorax on chest X-rays with fully convolutional multi-scale ScSE-DenseNet: a retrospective study

2020 ◽  
Vol 20 (S14) ◽  
Author(s):  
Qingfeng Wang ◽  
Qiyu Liu ◽  
Guoting Luo ◽  
Zhiqin Liu ◽  
Jun Huang ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Automatic Segmentation ◽  
Medical Emergency ◽  
Dice Similarity Coefficient ◽  
X Rays ◽  
X Ray ◽  
Learning Framework ◽  
Multi Scale ◽  
Life Threatening ◽  
Chest X Ray

Abstract Background Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiographs. However, the pneumothoraces in chest X-rays may be very subtle with highly variable in shape and overlapped with the ribs or clavicles, which are often difficult to identify. Our objective was to create a large chest X-ray dataset for pneumothorax with pixel-level annotation and to train an automatic segmentation and diagnosis framework to assist radiologists to identify pneumothorax accurately and timely. Methods In this study, an end-to-end deep learning framework is proposed for the segmentation and diagnosis of pneumothorax on chest X-rays, which incorporates a fully convolutional DenseNet (FC-DenseNet) with multi-scale module and spatial and channel squeezes and excitation (scSE) modules. To further improve the precision of boundary segmentation, we propose a spatial weighted cross-entropy loss function to penalize the target, background and contour pixels with different weights. Results This retrospective study are conducted on a total of eligible 11,051 front-view chest X-ray images (5566 cases of PTX and 5485 cases of Non-PTX). The experimental results show that the proposed algorithm outperforms the five state-of-the-art segmentation algorithms in terms of mean pixel-wise accuracy (MPA) with $$0.93\pm 0.13$$ 0.93 ± 0.13 and dice similarity coefficient (DSC) with $$0.92\pm 0.14$$ 0.92 ± 0.14 , and achieves competitive performance on diagnostic accuracy with 93.45% and $$F_1$$ F 1 -score with 92.97%. Conclusion This framework provides substantial improvements for the automatic segmentation and diagnosis of pneumothorax and is expected to become a clinical application tool to help radiologists to identify pneumothorax on chest X-rays.

scholarly journals SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images

2019 ◽  
Vol 75 ◽  
pp. 66-73 ◽  
Author(s):  
Han Liu ◽  
Lei Wang ◽  
Yandong Nan ◽  
Faguang Jin ◽  
Qi Wang ◽  
...  
Keyword(s):  
Disease Classification ◽  
Thoracic Disease ◽  
X Ray ◽  
Chest X Ray

scholarly journals CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

2021 ◽  
Author(s):  
Md. Saikat Islam Khan ◽  
Anichur Rahman ◽  
Md. Razaul Karim ◽  
Nasima Islam Bithi ◽  
Shahab Band ◽  
...  
Keyword(s):  
Healthcare Professionals ◽  
Feature Fusion ◽  
State Of The Art ◽  
Research Community ◽  
Diagnostic Model ◽  
X Ray ◽  
Multi Scale ◽  
The World ◽  
Chest X Ray ◽  
Learning Concept

The COVID-19 pandemic is an emerging respiratory infectious disease, having a significant impact on the health and life of many people around the world. Therefore, early identification of COVID-19 patients is the fastest way to restrain the spread of the pandemic. However, as the number of cases grows at an alarming pace, most developing countries are now facing a shortage of medical resources and testing kits. Besides, using testing kits to detect COVID-19 cases is a time-consuming, expensive, and cumbersome procedure. Faced with these obstacles, most physicians, researchers, and engineers have advocated for the advancement of computer-aided deep learning models to assist healthcare professionals in quickly and inexpensively recognize COVID-19 cases from chest X-ray (CXR) images. With this motivation, this paper proposes a CovidMulti-Net architecture based on the transfer learning concept to classify COVID-19 cases from normal and other pneumonia cases using three publicly available datasets that include 1341, 1341, and 446 CXR images from healthy samples and 902, 1564, and 1193 CXR images infected with Viral Pneumonia, Bacterial Pneumonia, and COVID-19 diseases. In the proposed framework, features from CXR images are extracted using three well-known pre-trained models, including DenseNet-169, ResNet-50, and VGG-19. The extracted features are then fed into a concatenate layer, making a robust hybrid model. The proposed framework achieved a classification accuracy of 99.4%, 95.2%, and 94.8% for 2-Class, 3-Class, and 4-Class datasets, exceeding all the other state-of-the-art models. These results suggest that the CovidMulti-Net frameworks ability to discriminate individuals with COVID-19 infection from healthy ones and provides the opportunity to be used as a diagnostic model in clinics and hospitals. We also made all the materials publicly accessible for the research community at: https://github.com/saikat15010/CovidMulti-Net-Architecture.git.

scholarly journals Chest X-Ray Image Preprocessing for Disease Classification

2021 ◽  
Vol 192 ◽  
pp. 658-665
Author(s):  
Guy Caseneuve ◽  
Iren Valova ◽  
Nathan LeBlanc ◽  
Melanie Thibodeau
Keyword(s):  
Disease Classification ◽  
Image Preprocessing ◽  
X Ray ◽  
Chest X Ray
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