TBDLNet: a network for classifying multidrug-resistant and
drug-sensitive tuberculosis
- URL: http://arxiv.org/abs/2310.18222v1
- Date: Fri, 27 Oct 2023 15:51:33 GMT
- Title: TBDLNet: a network for classifying multidrug-resistant and
drug-sensitive tuberculosis
- Authors: Ziquan Zhu, Jing Tao, Shuihua Wang, Xin Zhang, Yudong Zhang
- Abstract summary: The TBDLNet is suitable for classifying multidrug-resistant tuberculosis and drug-sensitive tuberculosis.
It can detect multidrug-resistant pulmonary tuberculosis as early as possible, which helps to adjust the treatment plan in time and improve the treatment effect.
- Score: 20.278451340628624
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper proposes applying a novel deep-learning model, TBDLNet, to
recognize CT images to classify multidrug-resistant and drug-sensitive
tuberculosis automatically. The pre-trained ResNet50 is selected to extract
features. Three randomized neural networks are used to alleviate the
overfitting problem. The ensemble of three RNNs is applied to boost the
robustness via majority voting. The proposed model is evaluated by five-fold
cross-validation. Five indexes are selected in this paper, which are accuracy,
sensitivity, precision, F1-score, and specificity. The TBDLNet achieves 0.9822
accuracy, 0.9815 specificity, 0.9823 precision, 0.9829 sensitivity, and 0.9826
F1-score, respectively. The TBDLNet is suitable for classifying
multidrug-resistant tuberculosis and drug-sensitive tuberculosis. It can detect
multidrug-resistant pulmonary tuberculosis as early as possible, which helps to
adjust the treatment plan in time and improve the treatment effect.
Related papers
- Enhancing mTBI Diagnosis with Residual Triplet Convolutional Neural
Network Using 3D CT [1.0621519762024807]
We introduce an innovative approach to enhance mTBI diagnosis using 3D Computed Tomography (CT) images.
We propose a Residual Triplet Convolutional Neural Network (RTCNN) model to distinguish between mTBI cases and healthy ones.
Our RTCNN model shows promising performance in mTBI diagnosis, achieving an average accuracy of 94.3%, a sensitivity of 94.1%, and a specificity of 95.2%.
arXiv Detail & Related papers (2023-11-23T20:41:46Z) - Can pruning improve certified robustness of neural networks? [106.03070538582222]
We show that neural network pruning can improve empirical robustness of deep neural networks (NNs)
Our experiments show that by appropriately pruning an NN, its certified accuracy can be boosted up to 8.2% under standard training.
We additionally observe the existence of certified lottery tickets that can match both standard and certified robust accuracies of the original dense models.
arXiv Detail & Related papers (2022-06-15T05:48:51Z) - An Efficient End-to-End Deep Neural Network for Interstitial Lung
Disease Recognition and Classification [0.5424799109837065]
This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns.
The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function.
A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model.
arXiv Detail & Related papers (2022-04-21T06:36:10Z) - Automated Atrial Fibrillation Classification Based on Denoising Stacked
Autoencoder and Optimized Deep Network [1.7403133838762446]
The incidences of atrial fibrillation (AFib) are increasing at a daunting rate worldwide.
For the early detection of the risk of AFib, we have developed an automatic detection system based on deep neural networks.
An end-to-end model is proposed to denoise the electrocardiogram signals using denoising autoencoders (DAE)
arXiv Detail & Related papers (2022-01-26T21:45:48Z) - Detection of Large Vessel Occlusions using Deep Learning by Deforming
Vessel Tree Segmentations [5.408694811103598]
This work uses convolutional neural networks for case-level classification trained with elastic deformation of the vessel tree segmentation masks to artificially augment training data.
The neural network classifies the presence of an LVO and the affected hemisphere.
In a 5-fold cross validated ablation study, we demonstrate that the use of the suggested augmentation enables us to train robust models even from few data sets.
arXiv Detail & Related papers (2021-12-03T09:07:29Z) - A Generic Deep Learning Based Cough Analysis System from Clinically
Validated Samples for Point-of-Need Covid-19 Test and Severity Levels [85.41238731489939]
We seek to evaluate the detection performance of a rapid primary screening tool of Covid-19 based on the cough sound from 8,380 clinically validated samples.
Our proposed generic method is an algorithm based on Empirical Mode Decomposition (EMD) with subsequent classification based on a tensor of audio features.
Two different versions of DeepCough based on the number of tensor dimensions, i.e. DeepCough2D and DeepCough3D, have been investigated.
arXiv Detail & Related papers (2021-11-10T19:39:26Z) - Osteoporosis Prescreening using Panoramic Radiographs through a Deep
Convolutional Neural Network with Attention Mechanism [65.70943212672023]
Deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs.
dataset of 70 panoramic radiographs (PRs) from 70 different subjects of age between 49 to 60 was used.
arXiv Detail & Related papers (2021-10-19T00:03:57Z) - Revisiting 3D Context Modeling with Supervised Pre-training for
Universal Lesion Detection in CT Slices [48.85784310158493]
We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices.
With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset.
The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
arXiv Detail & Related papers (2020-12-16T07:11:16Z) - Classification of COVID-19 in CT Scans using Multi-Source Transfer
Learning [91.3755431537592]
We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans.
With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet.
Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
arXiv Detail & Related papers (2020-09-22T11:53:06Z) - CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors
and Efficient Neural Networks [51.589769497681175]
The novel coronavirus (SARS-CoV-2) has led to a pandemic.
The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands.
We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
arXiv Detail & Related papers (2020-07-20T21:47:28Z) - Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images
and Deep Convolutional Neural Networks [0.0]
coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries.
There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily.
Five pre-trained convolutional neural network based models have been proposed for the detection of coronavirus pneumonia infected patient using chest X-ray radiographs.
arXiv Detail & Related papers (2020-03-24T13:50:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.