Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has led to global health and healthcare crisis, apart from the tremendous socioeconomic effects. One of the significant challenges in this crisis is to identify and monitor the COVID-19 patients quickly and efficiently to facilitate timely decisions for their treatment, monitoring, and management. Research efforts are on to develop less time-consuming methods to replace or to supplement RT-PCR-based methods. The present study is aimed at creating efficient deep learning models, trained with chest X-ray images, for rapid screening of COVID-19 patients. We used publicly available PA chest X-ray images of adult COVID-19 patients for the development of Artificial Intelligence (AI)-based classification models for COVID-19 and other major infectious diseases. To increase the dataset size and develop generalized models, we performed 25 different types of augmentations on the original images. Furthermore, we utilized the transfer learning approach for the training and testing of the classification models. The combination of two best-performing models (each trained on 286 images, rotated through 120° or 140° angle) displayed the highest prediction accuracy for normal, COVID-19, non-COVID-19, pneumonia, and tuberculosis images. AI-based classification models trained through the transfer learning approach can efficiently classify the chest X-ray images representing studied diseases. Our method is more efficient than previously published methods. It is one step ahead towards the implementation of AI-based methods for classification problems in biomedical imaging related to COVID-19.

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Automated pneumonia detection on chest X-ray images: A deep learning approach with different optimizers and transfer learning architectures

Measurement ◽

10.1016/j.measurement.2021.109953 ◽

2021 ◽

pp. 109953

Author(s):

Adhiyaman Manickam ◽

Jianmin Jiang ◽

Yu Zhou ◽

Abhinav Sagar ◽

Rajkumar Soundrapandiyan ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Learning Approach ◽

X Ray ◽

Chest X Ray ◽

Learning Architectures

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Automated detection of pneumonia cases using deep transfer learning with paediatric chest X-ray images

British Journal of Radiology ◽

10.1259/bjr.20201263 ◽

2021 ◽

pp. 20201263

Author(s):

Mohammad Salehi ◽

Reza Mohammadi ◽

Hamed Ghaffari ◽

Nahid Sadighi ◽

Reza Reiazi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Area Under Curve ◽

Binary Classification ◽

Classification Performance ◽

Learning Approach ◽

Insufficient Data ◽

X Ray ◽

Chest X Ray

Objective: Pneumonia is a lung infection and causes the inflammation of the small air sacs (Alveoli) in one or both lungs. Proper and faster diagnosis of pneumonia at an early stage is imperative for optimal patient care. Currently, chest X-ray is considered as the best imaging modality for diagnosing pneumonia. However, the interpretation of chest X-ray images is challenging. To this end, we aimed to use an automated convolutional neural network-based transfer-learning approach to detect pneumonia in paediatric chest radiographs. Methods: Herein, an automated convolutional neural network-based transfer-learning approach using four different pre-trained models (i.e. VGG19, DenseNet121, Xception, and ResNet50) was applied to detect pneumonia in children (1–5 years) chest X-ray images. The performance of different proposed models for testing data set was evaluated using five performances metrics, including accuracy, sensitivity/recall, Precision, area under curve, and F1 score. Results: All proposed models provide accuracy greater than 83.0% for binary classification. The pre-trained DenseNet121 model provides the highest classification performance of automated pneumonia classification with 86.8% accuracy, followed by Xception model with an accuracy of 86.0%. The sensitivity of the proposed models was greater than 91.0%. The Xception and DenseNet121 models achieve the highest classification performance with F1-score greater than 89.0%. The plotted area under curve of receiver operating characteristics of VGG19, Xception, ResNet50, and DenseNet121 models are 0.78, 0.81, 0.81, and 0.86, respectively. Conclusion: Our data showed that the proposed models achieve a high accuracy for binary classification. Transfer learning was used to accelerate training of the proposed models and resolve the problem associated with insufficient data. We hope that these proposed models can help radiologists for a quick diagnosis of pneumonia at radiology departments. Moreover, our proposed models may be useful to detect other chest-related diseases such as novel Coronavirus 2019. Advances in knowledge: Herein, we used transfer learning as a machine learning approach to accelerate training of the proposed models and resolve the problem associated with insufficient data. Our proposed models achieved accuracy greater than 83.0% for binary classification.

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An ensemble deep transfer-learning approach to identify COVID-19 cases from chest X-ray images

2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) ◽

10.1109/cibcb48159.2020.9277695 ◽

2020 ◽

Author(s):

Taki Hasan Rafi

Keyword(s):

Transfer Learning ◽

Learning Approach ◽

X Ray ◽

Chest X Ray

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COVID-19 Chest X-RAY detection : A transfer learning approach

10.22541/au.160513358.82993050/v1 ◽

2020 ◽

Author(s):

Ashwin Rachha

Keyword(s):

Transfer Learning ◽

Learning Approach ◽

X Ray ◽

Chest X Ray

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Using Transfer Learning Method to Develop an Artificial Intelligence Assisted Triaging for Endotracheal Tube Position on Chest X-ray

Diagnostics ◽

10.3390/diagnostics11101844 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1844

Author(s):

Kuo-Ching Yuan ◽

Lung-Wen Tsai ◽

Kevin S. Lai ◽

Sing-Teck Teng ◽

Yu-Sheng Lo ◽

...

Keyword(s):

Artificial Intelligence ◽

Transfer Learning ◽

Region Of Interest ◽

Learning Method ◽

Icu Patients ◽

Endotracheal Tubes ◽

X Ray ◽

Tube Position ◽

Ventilation Efficiency ◽

Chest X Ray

Endotracheal tubes (ETTs) provide a vital connection between the ventilator and patient; however, improper placement can hinder ventilation efficiency or injure the patient. Chest X-ray (CXR) is the most common approach to confirming ETT placement; however, technicians require considerable expertise in the interpretation of CXRs, and formal reports are often delayed. In this study, we developed an artificial intelligence-based triage system to enable the automated assessment of ETT placement in CXRs. Three intensivists performed a review of 4293 CXRs obtained from 2568 ICU patients. The CXRs were labeled “CORRECT” or “INCORRECT” in accordance with ETT placement. A region of interest (ROI) was also cropped out, including the bilateral head of the clavicle, the carina, and the tip of the ETT. Transfer learning was used to train four pre-trained models (VGG16, INCEPTION_V3, RESNET, and DENSENET169) and two models developed in the current study (VGG16_Tensor Projection Layer and CNN_Tensor Projection Layer) with the aim of differentiating the placement of ETTs. Only VGG16 based on ROI images presented acceptable performance (AUROC = 92%, F1 score = 0.87). The results obtained in this study demonstrate the feasibility of using the transfer learning method in the development of AI models by which to assess the placement of ETTs in CXRs.

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Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images

10.1101/2020.12.14.20248158 ◽

2020 ◽

Author(s):

Iason Katsamenis ◽

Eftychios Protopapadakis ◽

Athanasios Voulodimos ◽

Anastasios Doulamis ◽

Nikolaos Doulamis

Keyword(s):

Neural Networks ◽

Transfer Learning ◽

Bacterial Pneumonia ◽

Large Scale ◽

Training Data ◽

Classification Models ◽

Automated Diagnosis ◽

X Ray ◽

Learning Framework ◽

Chest X Ray

We introduce a deep learning framework that can detect COVID-19 pneumonia in thoracic radiographs, as well as differentiate it from bacterial pneumonia infection. Deep classification models, such as convolutional neural networks (CNNs), require large-scale datasets in order to be trained and perform properly. Since the number of X-ray samples related to COVID-19 is limited, transfer learning (TL) appears as the go-to method to alleviate the demand for training data and develop accurate automated diagnosis models. In this context, networks are able to gain knowledge from pretrained networks on large-scale image datasets or alternative data-rich sources (i.e. bacterial and viral pneumonia radiographs). The experimental results indicate that the TL approach outperforms the performance obtained without TL, for the COVID-19 classification task in chest X-ray images.

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Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

International Journal of E-Health and Medical Communications ◽

10.4018/ijehmc.20220701.oa2 ◽

2022 ◽

Vol 13 (2) ◽

pp. 1-11

Author(s):

Malathy Jawahar ◽

L. Jani Anbarasi ◽

Prassanna Jayachandran ◽

Manikandan Ramachandran ◽

Fadi Al-Turjman

Keyword(s):

Transfer Learning ◽

Classification Accuracy ◽

Hybrid Algorithm ◽

Vital Role ◽

Support Vector ◽

Learning Approach ◽

X Ray ◽

High Classification Accuracy ◽

Chest X Ray ◽

Different Parts

Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.

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