Lung lesions detection from CT images based on the modified Faster R-CNN

Xu, Linlin; Mao, Xuemin; Sun, Minmin; Li, Wentao; Wang, Yifan; Tang, Yuyang

doi:10.1109/cits49457.2020.9232611

Cited by 3 publications

(2 citation statements)

References 11 publications

(12 reference statements)

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“…Faster RCNN is a model for detecting lesions in CT images of patients with COVID‐19, as well as keeping track of their condition. It uses the K means++ for clustering the bounding box dimensions for better performance 78 …”

Section: Approaches For Covid‐19 Diagnosismentioning

confidence: 99%

COVID‐19: A systematic review of prediction and classification techniques

Varma,

Kalra,

Kirmani

2023

Int J Imaging Syst Tech

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COVID‐19 has affected more than 760 million people all over the world, as per the latest record of the WHO. The rapid proliferation of COVID‐19 patients not only created a health emergency but also led to an economic crisis. An early and accurate diagnosis of COVID‐19 can help in combating this deadly virus. In line with this, researchers have proposed several machine learning (ML) and deep learning (DL) techniques for detecting COVID‐19 since 2020. This article presents currently available manual diagnosis methods along with their limitations. It also provides an extensive survey of ML and DL techniques that can support medical professionals in the precise diagnosis of COVID‐19. ML methods, namely K‐nearest neighbor, support vector machine (SVM), artificial neural network, decision tree, naive bayes, and DL methods, viz. deep neural network, convolutional neural network (CNN), region‐based convolutional neural network, and long short‐term memories, are explored. It also provides details of the latest COVID‐19 open‐source datasets, consisting of x‐ray and computed tomography scan images. A comparative analysis of ML and DL techniques developed for COVID‐19 detection in terms of methodology, datasets, sample size, type of classification, performance, and limitations is also done. It has been found that SVM is the most frequently used ML technique, while CNN is the most commonly used DL technique for COVID‐19 detection. The challenges of an existing dataset have been identified, including size and quality of datasets, lack of labeled datasets, severity level, data imbalance, and privacy concerns. It is recommended that there is a need to establish a benchmark dataset that overcomes these challenges to enhance the effectiveness of ML and DL techniques. Further, hurdles in implementing ML and DL techniques in real‐time clinical settings have also been highlighted. In addition, the motivation noticed from the existing methods has been considered for extending the research with an optimized DL model, which attained improved performance using statistical and deep features. The optimized deep model performs better than 90% based on efficient features and proper classifier tuning.

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Section: Approaches For Covid‐19 Diagnosismentioning

confidence: 99%

COVID‐19: A systematic review of prediction and classification techniques

Varma,

Kalra,

Kirmani

2023

Int J Imaging Syst Tech

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“…So, the accuracy of Faster CNN is to be increased for dataset with small image size, which will increase the performance. They further work in improving detection accuracy and model structure [9].…”

Section: Introductionmentioning

confidence: 99%

Analysis of COVID-19 and Pneumonia Detection in Chest X-Ray Images using Deep Learning

Radha

2021

2021 International Conference on Communication, Control and Information Sciences (ICCISc)

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The on-going COVID-19 outbreak made healthcare systems across the globe to be in the edge of the battle. Recent stats indicate that more than 140+ million confirmed cases are diagnosed globally as of April 2021. The cases are increasing day by day. The early and auto diagnosis helps people to be precautious. The proposed work aims to detect COVID-19 patients and Pneumonia patients from X-Rays which is one of the medical imaging modes to analyse the health of patient's lung inflammation. The suitable Convolutional Neural Network Model is selected for the identified dataset. The model detects COVID-19 patients and Pneumonia patients on the real-world dataset of lung X-Ray images. Images are pre-processed and trained for various classifications like Normal, COVID-19 and Pneumonia. After pre-processing, the detection of the disease is done by selecting the appropriate features from the images in each of the datasets. The result indicates that accuracy of detection of COVID vs Normal and COVID vs Pneumonia. Among those two, COVID vs Normal is with better accuracy than COVID vs Pneumonia. This method detects not only COVID or Pneumonia, but also the subtypes of Pneumonia as bacterial or Viral Pneumonia with 80% and 91.46% respectively. The detection of COVID, Bacterial Pneumonia and Viral Pneumonia using the proposed model helps in rapid diagnosis and to distinguish COVID from Pneumonia and its types which facilitates to use appropriate and fast solutions.

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Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection

Canbay,

Adsiz,

Canbay

2024

Applied Sciences

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This paper introduces a new privacy-preserving transfer learning framework for the classification of kidney diseases. In the proposed framework, transfer learning is employed for feature extraction, and differential privacy is used to obtain noisy gradients. A variety of CNN architectures, including Xception, ResNet50, InceptionResNetV2, MobileNet, DenseNet201, InceptionV3, and VGG19 are utilized to evaluate the proposed framework. Analysis of a large dataset of 12,400 labeled kidney CT images shows that transfer learning architectures based on the proposed framework achieve excellent accuracy ratios in privacy-preserving classification. These results demonstrate the effectiveness of the proposed framework in enabling transfer learning models to classify kidney diseases while ensuring privacy. The MobileNet architecture stands out for its exceptional performance, with an impressive accuracy of 99.83% in privacy-preserving classification. Considering the findings of this study, it is evident that the proposed framework is appropriate for the early and private diagnosis of kidney diseases and promotes the achievement of promising results in this field.

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Lung lesions detection from CT images based on the modified Faster R-CNN

Cited by 3 publications

References 11 publications

COVID‐19: A systematic review of prediction and classification techniques

COVID‐19: A systematic review of prediction and classification techniques

Analysis of COVID-19 and Pneumonia Detection in Chest X-Ray Images using Deep Learning

Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection

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