A Review: Deep Learning Classification Performance of Normal and COVID-19 Chest X-ray Images

Abstract COVID19 chest X-ray has been used as supplementary tools to support COVID19 severity level diagnosis. However, there are challenges that required to face by researchers around the world in order to implement these chest X-ray samples to be very helpful to detect the disease. Here, this paper presents a review of COVID19 chest X-ray classification using deep learning approach. This study is conducted to discuss the source of images and deep learning models as well as its performances. At the end of this paper, the challenges and future work on COVID19 chest X-ray are discussed and proposed.

Download Full-text

Pneumonia Detection from Chest X-Rays using Neural Networks

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36489 ◽

2021 ◽

Vol 9 (VII) ◽

pp. 910-913

Author(s):

Ashish Jain

Keyword(s):

Deep Learning ◽

Learning Approach ◽

Great Success ◽

X Rays ◽

X Ray ◽

Open Set ◽

The World ◽

Chest X Ray ◽

Bacteria And Fungi ◽

Inflammation Of The Lungs

Pneumonia is one of the most serious diseases which cause the most deaths in the world. Viruses, bacteria, and fungi can cause pneumonia. The infection from spreading to the lungs in the human body. In order to diagnose this infection, a chest x-ray is carried out. The doctor uses X-ray image in order to diagnose or monitor the treatment of states in which inflammation of the lungs. X-rays are also used in the diagnosis of diseases such as emphysema, lung cancer, cancer of the line, and pipe, and tuberculosis (tb). However, a diagnosis of pneumonia requiring medical experts to comment on its presence felt in the chest x-ray. For decades, the auto- diagnosis (CAD) systems have been used for the respiratory disease based on chest X-ray images. Deep learning allows machines can quickly extract and classify objects from a photo. Ilham, with the great success of deep learning, we use a deep learning approach to detection of pneumonia into the work. Convolutional neural network that was developed for this study is the inflammation of the lungs. Supervised learning is ANCHORED to the use of features and functions. In general, the data of 5826 images with the help of one of the Kaggle.com. The CNN training and testing, that is, an open set of data. In the proposed method, the high success rate of accurate classification is achieved.

Download Full-text

CLASSIFICATION PERFORMANCE COMPARISONS OF DEEP LEARNING MODELS IN PNEUMONIA DIAGNOSIS USING CHEST X-RAY IMAGES

Turkish Journal of Engineering ◽

10.31127/tuje.652358 ◽

2019 ◽

Author(s):

Osman Doğuş Gülgün ◽

Prof. Dr. Hamza EROL

Keyword(s):

Deep Learning ◽

Classification Performance ◽

Learning Models ◽

X Ray ◽

Chest X Ray ◽

Pneumonia Diagnosis ◽

Performance Comparisons

Download Full-text

COVID-19 Diagnosis from Chest X-ray Images Using Deep Learning Approach

2020 International Conference on Advanced Science and Engineering (ICOASE) ◽

10.1109/icoase51841.2020.9436614 ◽

2020 ◽

Author(s):

Noor N. Qaqos ◽

Omar S. Kareem

Keyword(s):

Deep Learning ◽

Learning Approach ◽

X Ray ◽

Chest X Ray

Download Full-text

Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images

Applied Sciences ◽

10.3390/app11094233 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4233

Author(s):

Biprodip Pal ◽

Debashis Gupta ◽

Md. Rashed-Al-Mahfuz ◽

Salem A. Alyami ◽

Mohammad Ali Moni

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Chest Radiography ◽

High Sensitivity ◽

Ct Images ◽

Classification Performance ◽

Learning Models ◽

X Ray ◽

Image Capturing ◽

Fast Gradient

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation; it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

Download Full-text

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

Download Full-text

Deep Learning Approach for Diagnosis of Thoracic Diseases from Chest X-Ray Images

2021 International Conference on Communication information and Computing Technology (ICCICT) ◽

10.1109/iccict50803.2021.9510133 ◽

2021 ◽

Author(s):

Siddhika Arunachalam ◽

Prasenjit Bhavathankar

Keyword(s):

Deep Learning ◽

Learning Approach ◽

X Ray ◽

Thoracic Diseases ◽

Chest X Ray

Download Full-text

A Mask-guided Attention Deep Learning Model for COVID-19 Diagnosis based on an Integrated CT Scan Images Database

10.36227/techrxiv.18166667.v1 ◽

2022 ◽

Author(s):

Maede Maftouni ◽

Bo Shen ◽

Andrew Chung Chee Law ◽

Niloofar Ayoobi Yazdi ◽

Zhenyu Kong

Keyword(s):

Deep Learning ◽

Ct Scan ◽

Imaging Modality ◽

Learning Model ◽

Classification Performance ◽

Computer Assisted ◽

Learning Approach ◽

Learning Models ◽

Task Learning ◽

Data Efficiency

The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images.The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task baseline and state-of-the-art models, as measured by various popular metrics. In our experiment with different percentages of data from our curated dataset, the classification performance gain from this multi-task learning approach is more significant for the smaller training sizes. Furthermore, experimental results demonstrate that our method enhances the focus on the lesions, as witnessed by bothattention and attribution maps, resulting in a more interpretable model.

Download Full-text

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.

Download Full-text