Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction

Afif, Mouna; Ayachi, Riadh; Said, Yahia; Atri, Mohamed

doi:10.1007/s11042-023-14941-w

“…From the experimental results, it is confirmed that the proposed model bring optimal results than the performance of the state of art. The accuracy value attained by the proposed model is found to be 96.23% as concluded in (Afif, 2023).…”

Section: Literature Reviewmentioning

confidence: 55%

Deep Feature Wise Attention Based Convolutional Neural Network for Covid-19 Detection Using Lung CT Scan Images

Yamathi

¹

,

Rani

²

,

Krishna

³

2023

JAETS

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with the help of effective DL(Deep Learning) based algorithms. Though several clinical procedures and imaging modalities exists to diagnose Covid-19, these methods are time-consuming processes and sometimes the predictions are incorrect. Concurrently, AI (Artificial Intelligence) based DL models have gained attention in this area due to its innate capability for efficient learning. Though conventional systems have tried to perform better prediction, they lacked in accuracy with prediction rate. Moreover, the conventional systems have not utilized attention model completely for Covid-19 detection. This research is intended to resolve these pitfalls of covid-19 detection methods with the help of deep feature wise attention based Convolutional Neural Network. For this purpose, the data has been pre-processed by image resizing, the Residual Descriptor with Conv-BAM(Convolutional Block Attention Module) has been employed to obtain refined features from spatial and channel wise attention based module. The obtained features are used in the present study to improvise the classification as covid positive or negative. The performance of the proposed system has been assessed with regard to metrics to prove better efficiency. The proposed method achieved high accuracy rate of 97.82%. This DL based model can be used as a supplementary tool in the diagnosis of Covid-19 alongside other diagnostic method

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“…Interpretability is key to medical diagnosis, and understanding the reasons for decisions is viewed as important as the need for decisions [50]. Interpretable machine learning and deep learning techniques are actively being researched to address this issue [51][52][53], but they remain a challenge in many applications.…”

Section: Challengesmentioning

confidence: 99%

Recent Machine Learning and Deep Learning Theories and Methods for COVID-19 Diagnosis

Tang

2023

Preprint

0

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A long time has passed since COVID-19 was discovered and widely disseminated. Both machine learning and deep learning have moved towards the research of diagnosing COVID-19. Compared to deep learning, traditional machine learning-based methods can also achieve good diagnostic results if they are improved based on innovative points. And deep learning remains a more popular research object. Based on the deep neural network in the field of deep learning, the convolutional neural network, recurrent neural network, long and short-term memory network, and Transformer model have been extended. These models improve the performance and processing power of neural networks by introducing new structures and algorithms. This paper will introduce the basic concepts of machine learning and deep learning, as well as the details of using the relevant methods of both to help diagnose COVID-19.

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“…Moreover, the rapid evolution of the virus and the emergence of new variants pose a challenge in maintaining the accuracy and generalizability of machine learning and deep learning models. For example, SARS-CoV-2 is a virus that causes COVID-19, which is characterized by rapid transmission, constant change, and a higher probability of fatality [54]. These models are trained on historical data and may not perform as well when faced with novel variations of the virus [55].…”

Section: Challengesmentioning

confidence: 99%

Recent Machine Learning and Deep Learning Theories and Methods for COVID-19 Diagnosis

Tang

2023

Preprint

0

View full text Add to dashboard Cite

A long time has passed since COVID-19 was discovered and widely disseminated. Both machine learning and deep learning have moved towards the research of diagnosing COVID-19. Compared to deep learning, traditional machine learning-based methods can also achieve good diagnostic results if they are improved based on innovative points. And deep learning remains a more popular research object. Based on the deep neural network in the field of deep learning, the convolutional neural network, recurrent neural network, long and short-term memory network, and Transformer model have been extended. These models improve the performance and processing power of neural networks by introducing new structures and algorithms. This paper will introduce the basic concepts of machine learning and deep learning, as well as the details of using the relevant methods of both to help diagnose COVID-19.

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Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction

Cited by 10 publications

References 43 publications

Deep Feature Wise Attention Based Convolutional Neural Network for Covid-19 Detection Using Lung CT Scan Images

Deep Feature Wise Attention Based Convolutional Neural Network for Covid-19 Detection Using Lung CT Scan Images

Recent Machine Learning and Deep Learning Theories and Methods for COVID-19 Diagnosis

Recent Machine Learning and Deep Learning Theories and Methods for COVID-19 Diagnosis

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