Challenges, opportunities, and advances related to COVID-19 classification based on deep learning

Agnihotri, Abhishek; Kohli, Narendra

doi:10.1016/j.dsm.2023.03.005

Cited by 12 publications

(3 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Despite exhibiting high accuracies in training and validation, CNN-based transfer learning might underperform during the testing phase [37][38][39][40][41]. This could be due to overfitting, which is a common issue in medical image classification with a limited number of discriminatory image features [7,13,[42][43][44]. Prominent CNN models typically encompass over ten layers, boasting 60+ million trained parameters, and are trained on expansive datasets like ImageJ, which houses 1000 categories.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Talaat,

Si,

2023

Applied Sciences

View full text Add to dashboard Cite

This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform on test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training dataset with new images? (3) How can learned features elucidate classification results, and how do they vary among different models? To achieve these aims, four CNN models—AlexNet, ResNet-50, MobileNet, and VGG-19—were trained in five rounds by incrementally adding new images to a baseline training set comprising 11,538 chest X-ray images. In each round, the models were tested on four datasets with decreasing levels of image similarity. Notably, all models showed performance drops when tested on datasets containing outlier images or sourced from other clinics. In Round 1, 95.2~99.2% accuracy was achieved for the Level 1 testing dataset (i.e., from the same clinic but set apart for testing only), and 94.7~98.3% for Level 2 (i.e., from an external clinic but similar). However, model performance drastically decreased for Level 3 (i.e., outlier images with rotation or deformation), with the mean sensitivity plummeting from 99% to 36%. For the Level 4 testing dataset (i.e., from another clinic), accuracy decreased from 97% to 86%, and sensitivity from 99% to 67%. In Rounds 2 and 3, adding 25% and 50% of the outlier images to the training dataset improved the average Level-3 accuracy by 15% and 23% (i.e., from 56% to 71% to 83%). In Rounds 4 and 5, adding 25% and 50% of the external images increased the average Level-4 accuracy from 81% to 92% and 95%, respectively. Among the models, ResNet-50 demonstrated the most robust performance across the five-round training/testing phases, while VGG-19 persistently underperformed. Heatmaps and intermediate activation features showed visual correlations to COVID-19 and pneumonia X-ray manifestations but were insufficient to explicitly explain the classification. However, heatmaps and activation features at different rounds shed light on the progression of the models’ learning behavior.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Talaat,

Si,

2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Notably, deep learning methods, such as those referenced in, 4,[8][9][10][11][12][13][14] have made substantial contributions to the medical field. Numerous CNN (Convolutional Neural Network) based approaches have been employed for automated SLS, with a significant body of literature dedicated to optimizing their performance.…”

Section: Introductionmentioning

confidence: 99%

SRP&PASMLP‐Net: Lightweight skin lesion segmentation network based on structural re‐parameterization and parallel axial shift multilayer perceptron

Wei,

Chen,

Zhao

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Accurate skin lesion segmentation (SLS) plays an essential role in the computer‐aided diagnosis of skin diseases, for example, melanomas. However, the automated SLS is challenging due to variations in the nature of skin diseases, particularly the ambiguities of boundaries of skin lesion areas (SLAs) and occlusions by hairs. Though many deep learning models, represented by UNet, have been successfully applied to the SLS over the past years, most suffer from inaccurate SLA segmentation with heavy model parameters and slow inferencing speed, hindering their practical applications. To address these challenges, a lightweight SLS network based on structural re‐parameterization and parallel axial shift multilayer perceptron (MLP), called SRP&PASMLP‐Net, is proposed to achieve powerful segmentation performance with fast inference ability. Specifically, a kind of diverse convolution module, the Re‐parameterization Diversity Convolution (RDC) block, is proposed in the shallow stage of the segmentation network to enrich the feature space. The inference is performed by structural re‐parameterization techniques to decouple the structure at the training and inference stages, thus improving the performance of the model without any inference time cost. In addition, a parallel axial shift MLP (PASMLP) module is proposed, which captures local dependencies by axially shifting the channels of the feature map to capture the information flow from different axes and reduce the network parameters and computational complexity effectively. Extensive experimental results show that the proposed SRP&PASMLP‐Net can provide a 4‐fold improvement in the inference speed and a 17‐fold reduction in model parameters compared with the benchmark network, UNet. It outperforms the state‐of‐the‐art methods on two public datasets, that is, ISIC2018 and PH2, for the SLS.

show abstract

“…Zehra Karapinar Senturk proposed a deep-learning-based method to identify arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR) for cardiovascular diseases (CVD), and the recognition accuracy reached 98.7% [16]. Abhishek Agnihotri used machine learning methods to evaluate and identify COVID-19, putting forward the future development prospect of machine learning applied to medicine [17].…”

Section: Introductionmentioning

confidence: 99%

A Soybean Classification Method Based on Data Balance and Deep Learning

Zhang

2023

Applied Sciences

View full text Add to dashboard Cite

Soybean is a type of food crop with economic benefits. Whether they are damaged or not directly affects the survival and nutritional value of soybean plants. In machine learning, unbalanced data represent a major factor affecting machine learning efficiency, and unbalanced data refer to a category in which the number of samples in one category is much larger than that in the other, which biases the classification results towards a category with a large number of samples and thus affects the classification accuracy. Therefore, the effectiveness of the data-balancing method based on a convolutional neural network is investigated in this paper, and two balancing methods are used to expand the data set using the over-sampling method and using the loss function with assignable class weights. At the same time, to verify the effectiveness of the data-balancing method, four networks are introduced for control experiments. The experimental results show that the new loss function can effectively improve the classification accuracy and learning ability, and the classification accuracy of the DenseNet network can reach 98.48%, but the classification accuracy will be greatly reduced by using the data-augmentation method. With the binary classification method and the use of data-augmentation data sets, the excessive number of convolution layers will lead to a reduction in the classification accuracy and a small number of convolution layers can be used for classification purposes. It is verified that a neural network using a small convolution layer can improve the classification accuracy by 1.52% using the data-augmentation data-balancing method.

show abstract

Challenges, opportunities, and advances related to COVID-19 classification based on deep learning

Cited by 12 publications

References 49 publications

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

SRP&PASMLP‐Net: Lightweight skin lesion segmentation network based on structural re‐parameterization and parallel axial shift multilayer perceptron

A Soybean Classification Method Based on Data Balance and Deep Learning

Contact Info

Product

Resources

About