Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images

Şahin, Muhammet; Ulutaş, Hasan; Yüce, Esra; Erkoç, Mustafa

doi:10.1007/s00521-023-08450-y

Cited by 31 publications

(9 citation statements)

References 32 publications

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“…Similar results can also be found in Refs. [ [55] , [56] , [57] ]. These results further demonstrate the dominance of ResNet50-DeepLabv3+ in the task of image segmentation.…”

Section: Methodsmentioning

confidence: 99%

Semantic segmentation for tooth cracks using improved DeepLabv3+ model

Xie,

Lu,

Guo

et al. 2024

Heliyon

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“…Similar results can also be found in Refs. [ [55] , [56] , [57] ]. These results further demonstrate the dominance of ResNet50-DeepLabv3+ in the task of image segmentation.…”

Section: Methodsmentioning

confidence: 99%

Semantic segmentation for tooth cracks using improved DeepLabv3+ model

Xie,

Lu,

Guo

et al. 2024

Heliyon

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“…Sahin et al [28] investigated the application of DL methodologies for COVID-19 diagnosis utilizing CT imagery. Their approach leveraged Faster R-CNN and Mask R-CNN architectures for the classification of patients with COVID-19 and pneumonia.…”

Section: Dl-based Techniquesmentioning

confidence: 99%

COVID-19 Image Classification: A Comparative Performance Analysis of Hand-Crafted vs. Deep Features

Alinsaif

2024

Computation

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This study investigates techniques for medical image classification, specifically focusing on COVID-19 scans obtained through computer tomography (CT). Firstly, handcrafted methods based on feature engineering are explored due to their suitability for training traditional machine learning (TML) classifiers (e.g., Support Vector Machine (SVM)) when faced with limited medical image datasets. In this context, I comprehensively evaluate and compare 27 descriptor sets. More recently, deep learning (DL) models have successfully analyzed and classified natural and medical images. However, the scarcity of well-annotated medical images, particularly those related to COVID-19, presents challenges for training DL models from scratch. Consequently, I leverage deep features extracted from 12 pre-trained DL models for classification tasks. This work presents a comprehensive comparative analysis between TML and DL approaches in COVID-19 image classification.

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“…Supervised learning refers to training by using training data with known outputs. Sahin et al [4] selected 4000 lung computed tomography (CT) images from Yozgat Bozok University to automatically quantify the severity of coronavirus disease in CT images. At the same time, they also counted the classification losses for each region of interest, the classification losses and total training losses in the region proposal network, and the average accuracy proved the superior performance of the model.…”

Section: Research Status 21 Main Application Scenariosmentioning

confidence: 99%

Research progress of artificial intelligence in medical imaging field

Wang

2024

AETR

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With the rapid development of medical equipment, medical imaging has entered the era of big data. How to process massive data quickly and accurately extract critical information for disease diagnosis and treatment is a major challenge facing clinical practice. Artificial intelligence has unique advantages in real-time processing, prediction and analysis, transfer learning, etc., which provides a new breakthrough for solving this problem. This review reviews the artificial intelligence models applied to medical image processing in recent years, shows their wide application scenarios, briefly discusses their unique advantages from three aspects of image recognition, image enhancement and image registration, and finally summarizes the current research status and puts forward some prospects.

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Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images

Cited by 31 publications

References 32 publications

Semantic segmentation for tooth cracks using improved DeepLabv3+ model

Semantic segmentation for tooth cracks using improved DeepLabv3+ model

COVID-19 Image Classification: A Comparative Performance Analysis of Hand-Crafted vs. Deep Features

Research progress of artificial intelligence in medical imaging field

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