ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

Nahiduzzaman, Md.; Islam, Md. Rabiul; Hassan, Rakibul

doi:10.1016/j.eswa.2022.118576

Cited by 37 publications

(18 citation statements)

References 48 publications

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“…The experimental results are shown in Table 8. SOTA methods include DeCoVNet, 21 VSBN, 22 COVNet, 23 ResNet50, 24 COVID‐Net, 25 DenseNet121, 26 SVM, 27 VGG19, 28 DLM, 29 HTV 30 (homomorphic transformation and VGG), ChestX‐ray6, 31 and Dense‐CNN 32 …”

Section: Experiments and Results Analysismentioning

confidence: 99%

“…SOTA methods include DeCoVNet, 21 VSBN, 22 COVNet, 23 ResNet50, 24 COVID-Net, 25 DenseNet121, 26 SVM, 27 VGG19, 28 DLM, 29 HTV 30 (homomorphic transformation and VGG), ChestX-ray6, 31 and Dense-CNN. 32 By comparison, IRCM-CAPS shows good results in each of the performance indicators. Although individual indicators are slightly lower than those of other networks, overall, IRCM-CAP performs well on small datasets, which can help radiologists more accurately locate the location of suspected lesions greatly reduces the pressure on doctors to deal with the epidemic.…”

Section: Comparison With State-of-the-art Approachesmentioning

confidence: 95%

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IRCM‐Caps: An X‐ray image detection method for COVID‐19

Qiu

2023

Clinical Respiratory J

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Objective COVID‐19 is ravaging the world, but traditional reverse transcription‐polymerase reaction (RT‐PCR) tests are time‐consuming and have a high false‐negative rate and lack of medical equipment. Therefore, lung imaging screening methods are proposed to diagnose COVID‐19 due to its fast test speed. Currently, the commonly used convolutional neural network (CNN) model requires a large number of datasets, and the accuracy of the basic capsule network for multiple classification is limital. For this reason, this paper proposes a novel model based on CNN and CapsNet. Methods The proposed model integrates CNN and CapsNet. And attention mechanism module and multi‐branch lightweight module are applied to enhance performance. Use the contrast adaptive histogram equalization (CLAHE) algorithm to preprocess the image to enhance image contrast. The preprocessed images are input into the network for training, and ReLU was used as the activation function to adjust the parameters to achieve the optimal. Result The test dataset includes 1200 X‐ray images (400 COVID‐19, 400 viral pneumonia, and 400 normal), and we replace CNN of VGG16, InceptionV3, Xception, Inception‐Resnet‐v2, ResNet50, DenseNet121, and MoblieNetV2 and integrate with CapsNet. Compared with CapsNet, this network improves 6.96%, 7.83%, 9.37%, 10.47%, and 10.38% in accuracy, area under the curve (AUC), recall, and F1 scores, respectively. In the binary classification experiment, compared with CapsNet, the accuracy, AUC, accuracy, recall rate, and F1 score were increased by 5.33%, 5.34%, 2.88%, 8.00%, and 5.56%, respectively. Conclusion The proposed embedded the advantages of traditional convolutional neural network and capsule network and has a good classification effect on small COVID‐19 X‐ray image dataset.

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Section: Experiments and Results Analysismentioning

confidence: 99%

Section: Comparison With State-of-the-art Approachesmentioning

confidence: 95%

IRCM‐Caps: An X‐ray image detection method for COVID‐19

Qiu

2023

Clinical Respiratory J

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“…Some examples can be found in references [27] [42-52]. Except for the study [50], most models performed on 3 and 2class classifications. However, our model is better than [50] in all evaluation metrics (accuracy, precision, recall, and F-score).…”

Section: B Model Performancementioning

confidence: 99%

“…Except for the study [50], most models performed on 3 and 2class classifications. However, our model is better than [50] in all evaluation metrics (accuracy, precision, recall, and F-score). Furthermore, our training time is significantly less than it.…”

Section: B Model Performancementioning

confidence: 99%

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

Ali¹,

Zghal²,

Hariri³

et al. 2023

IJACSA

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In the last three years, the coronavirus (COVID-19) pandemic put healthcare systems worldwide under tremendous pressure. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing many diseases (for example, COVID-19). Recently, intelligent systems (Machine Learning (ML) and Deep Learning (DL)) have been widely utilized to identify COVID-19 from other upper respiratory diseases (such as viral pneumonia and lung opacity). Nevertheless, identifying COVID-19 from the CXR images is challenging due to similar symptoms. To improve the diagnosis of COVID-19 using CXR images, this article proposes a new deep neural network model called Fast Hybrid Deep Neural Network (FHDNN). FHDNN consists of various convolutional layers and various dense layers. In the beginning, we preprocessed the dataset, extracted the best features, and expanded it. Then, we converted it from two dimensions to one dimension to reduce training speed and hardware requirements. The experimental results demonstrate that preprocessing and feature expansion before applying FHDNN lead to better detection accuracy and reduced speedy execution. Furthermore, the model FHDNN outperformed the counterparts by achieving an accuracy of 99.9%, recall of 99.9%, F1-Score has 99.9%, and precision of 99.9% for the detection and classification of COVID-19. Accordingly, FHDNN is more reliable and can be considered a robust and faster model in COVID-19 detection.

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“…AI‐based deep learning approaches outperform and enhance traditional diagnostic methods by enabling automated analysis of medical images with high accuracy and efficiency 16 . Recent studies have explored the application of pre‐trained CNN models, 17–19 transfer learning, 20–24 and extreme/ensemble learning 25–27 techniques to improve the performance of multi‐class Lung disease detection from chest X‐rays. Transfer learning allows pre‐trained models to be fine‐tuned on smaller medical image datasets that enable CNN to perform efficient knowledge transfer and faster convergence during training.…”

Section: Introductionmentioning

confidence: 99%

Detection of multi‐class lung diseases based on customized neural network

Ali,

Wang,

Shi

2024

Computational Intelligence

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In the medical image processing domain, deep learning methodologies have outstanding performance for disease classification using digital images such as X‐rays, magnetic resonance imaging (MRI), and computerized tomography (CT). However, accurate diagnosis of disease by medical personnel can be challenging in certain cases, such as the complexity of interpretation and non‐availability of expert personnel, difficulty at pixel‐level analysis, etc. Computer‐aided diagnostic (CAD) systems with proper training have shown the potential to enhance diagnostic accuracy and efficiency. With the exponential growth of medical data, CAD systems can analyze and extract valuable information by assisting medical personnel during the disease diagnostic process. To overcome these challenges, this research introduces CX‐RaysNet, a novel deep‐learning framework designed for the automatic identification of various lung disease classes in digital chest X‐ray images. The core novelty of the CX‐RaysNet framework lies in the integration of both convolutional and group convolutional layers, along with the usage of small filter sizes and the incorporation of dropout regularization. This phenomenon helps us optimize the model's ability to distinguish minute features that reveal different lung diseases. Additionally, data augmentation techniques are implemented to augment the training and testing datasets, which enhances the model's robustness and generalizability. The performance evaluation of CX‐RaysNet reveals promising results, with the proposed model achieving a multi‐class classification accuracy of 97.25%. Particularly, this study represents the first attempt to optimize a model specifically for low‐power embedded devices, aiming to improve the accuracy of disease detection while minimizing computational resources.

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ChestX-Ray6: Prediction of multiple diseases including COVID-19 from chest X-ray images using convolutional neural network

Cited by 37 publications

References 48 publications

IRCM‐Caps: An X‐ray image detection method for COVID‐19

IRCM‐Caps: An X‐ray image detection method for COVID‐19

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

Detection of multi‐class lung diseases based on customized neural network

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