COVID-19 Infection Localization and Severity Grading from Chest X-ray Images

Tahir, Anas; Chowdhury, Muhammad E. H.; Khandakar, Amith; Rahman, Tawsifur; Qiblawey, Yazan; Khurshid, Uzair; Kıranyaz, Serkan; Ibtehaz, Nabil; Rahman, Muhammad; Al-Madeed, Somaya; Hameed, Khaled; Hamid, Tahir; Mahmud, Sakib; Ezeddin, Maymouna

doi:10.48550/arxiv.2103.07985

Cited by 2 publications

(4 citation statements)

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“…Deep learning is leveraging the field of computer vision in different aspects such as autonomous driving [20], biomedical applications [21][22][23], etc., to name a few. Seg-mentation and visualization techniques are often used in machine learning technique to confirm the reliability of the trained model and, in fact, segmentation has helped in improving the classification performance [24][25][26]. To increase the reliability of the classification models, semantic segmentation models are used in sign language [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Podder

Chowdhury

Tahir

et al. 2022

Sensors

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A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and deep learning research because sign language recognition accuracy may vary on the skin tone, hand orientation, and background. This research has used deep machine learning models for accurate and reliable BdSL Alphabets and Numerals using two well-suited and robust datasets. The dataset prepared in this study comprises of the largest image database for BdSL Alphabets and Numerals in order to reduce inter-class similarity while dealing with diverse image data, which comprises various backgrounds and skin tones. The papers compared classification with and without background images to determine the best working model for BdSL Alphabets and Numerals interpretation. The CNN model trained with the images that had a background was found to be more effective than without background. The hand detection portion in the segmentation approach must be more accurate in the hand detection process to boost the overall accuracy in the sign recognition. It was found that ResNet18 performed best with 99.99% accuracy, precision, F1 score, sensitivity, and 100% specificity, which outperforms the works in the literature for BdSL Alphabets and Numerals recognition. This dataset is made publicly available for researchers to support and encourage further research on Bangla Sign Language Interpretation so that the hearing and speech-impaired individuals can benefit from this research.

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Section: Literature Reviewmentioning

confidence: 99%

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Podder

Chowdhury

Tahir

et al. 2022

Sensors

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“…The death and the transmission rate by this virus are very high, and it can survive a few hours to few days in the environment. The primary symptoms of COVID-19 are fever, cough, headache, muscle pain, and shortness of breath [13,17,24]. It spread so fast from Wuhan china to the whole world that it declared it a global pandemic [18,21].…”

Section: Introductionmentioning

confidence: 99%

“…Comparative Study of Deep-Learning Models for COVID-19 Diagnosis have observed that X-ray mainly was used for the theoretical implementation of deep learning as CT images pose several challenges in detecting COVID infected region using deep learning algorithms. The challenges imposed by CT images are less applicability, slower image acquisition, high cost, limited sensitivity [24]. On the contrary, chest x ray-based ML models for COVID-19 diagnosis has become the popular early detection technique recent days because of its various salient features like it can be used in emergencies using portable device [25], cheaper, faster and reliable method [24].…”

Section: Introductionmentioning

confidence: 99%

“…The challenges imposed by CT images are less applicability, slower image acquisition, high cost, limited sensitivity [24]. On the contrary, chest x ray-based ML models for COVID-19 diagnosis has become the popular early detection technique recent days because of its various salient features like it can be used in emergencies using portable device [25], cheaper, faster and reliable method [24]. An automated, faster, and reliable COVID-19 diagnosis method algorithm with better performance can save the RT-PCR kits [20].…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Deep Learning Models for COVID-19 Diagnosis Based on X-Ray Images

Siddiqui

Hossain²,

Ferdous³

et al. 2022

Advances in Sustainability Science and Technology

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Background: The rise of COVID-19 has caused immeasurable loss to public health globally. The world has faced a severe shortage of the goldstandard testing kit known as RT-PCR (Reverse Transcription Polymerase Chain Reaction). The accuracy of RT-PCR is not 100%, and it takes a few hours to deliver the test results. An additional testing solution to RT-PCR would be beneficial. Deep learning's superiority in image processing is characterized as the most effective COVID-19 diagnosis based on images. The small number of COVID-19 X-ray images in existing deep-learning methods for COVID-19 diagnosis may degrade the performance of deep-learning methods for new sets of images. Our priority for this research is to test and compare different deep-learning algorithms on a dataset consisting of many COVID-19 X-ray images. Methods: We have merged the publicly available image data into two groups (COVID and Normal). Our dataset contains 579 COVID-19 cases and 1773 Normal cases of X-ray images. We have used 145 COVID-19 cases and 150 Normal cases to test the deep-learning models. Deeplearning models based on CNN, VGG16 and 19, and InceptionV3 have been considered for prediction. The performance of these models is compared based on measurements of accuracy, sensitivity, and specificity. In the deep-learning models, the SoftMax activation function is used along with the Adam optimiser and categorical cross-entropy loss. A customised hybrid CNN model found in literature is considered and compared to explore how the inclusion of many COVID-19 X-ray images could impact the model's performance. Results: The accuracy of the considered deep-learning models using InceptionV3, VGG16, and VGG19 algorithms achieved 50%, 90%, and 83%, respectively, in predicting the X-ray images of COVID-19. We have shown that number of COVID-19 X-ray images does have a significant Shah Siddiqui et al.impact on the model's performance. A customised hybrid CNN model found in the literature failed to perform well on a dataset consisting of a large number of COVID-19 X-ray images. The customised hybrid CNN model reached an accuracy of 71% on many COVID-19 X-ray images. In contrast, it achieved 98% accuracy on a small number of COVID-19 X-ray images. It is also observed from the experiments that the VGG16 performs well with an increased number of images. Conclusions: A maximized number of COVID-19 X-ray images should be considered in building a deep-learning model. The deep-learning model with VGG16 performs the best in predicting from the X-ray images.

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COVID-19 Infection Localization and Severity Grading from Chest X-ray Images

Cited by 2 publications

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Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

A Comparative Study of Deep Learning Models for COVID-19 Diagnosis Based on X-Ray Images

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