Deep Learning for COVID-19 Diagnosis from CT Images

Loddo, Andrea; Pili, Fabio; Ruberto, Cecilia Di

doi:10.3390/app11178227

Cited by 23 publications

(30 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Loddo et al [29] proposed a COVID-19 diagnosis method employing CNN different architectures for accurate detection of COVID-19. In the proposed method development, two CT scan images data sets COVIDx CT-2A and COVID-CT have incorporated for evaluation of proposed model.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

Haq

Ahmad

et al. 2021

Sensors

View full text Add to dashboard Cite

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread. The automatic diagnosis system is significantly necessary for COVID-19 detection. To diagnose COVID-19 from chest X-ray images, employing artificial intelligence techniques based methods are more effective and could correctly diagnosis it. The existing diagnosis methods of COVID-19 have the problem of lack of accuracy to diagnosis. To handle this problem we have proposed an efficient and accurate diagnosis model for COVID-19. In the proposed method, a two-dimensional Convolutional Neural Network (2DCNN) is designed for COVID-19 recognition employing chest X-ray images. Transfer learning (TL) pre-trained ResNet-50 model weight is transferred to the 2DCNN model to enhanced the training process of the 2DCNN model and fine-tuning with chest X-ray images data for final multi-classification to diagnose COVID-19. In addition, the data augmentation technique transformation (rotation) is used to increase the data set size for effective training of the R2DCNNMC model. The experimental results demonstrated that the proposed (R2DCNNMC) model obtained high accuracy and obtained 98.12% classification accuracy on CRD data set, and 99.45% classification accuracy on CXI data set as compared to baseline methods. This approach has a high performance and could be used for COVID-19 diagnosis in E-Healthcare systems.

show abstract

Section: Introductionmentioning

confidence: 99%

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

Haq

Ahmad

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Here, we have evaluated the categorization results of the DenseNet‐77 with several latest approaches employing the same dataset for COVID‐19 classification. To conduct this evaluation, we compared the average classification performance of the presented approach with the average accuracy values demonstrated in these works (Gunraj et al, 2021 ; Loddo et al, 2021 ; Zhao et al, 2021 ). The comparative analysis is shown in Table 7 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

COVID‐DAI: A novel framework forCOVID‐19 detection and infection growth estimation using computed tomography images

Nazir

Nawaz

Javed

et al. 2022

Microscopy Res & Technique

View full text Add to dashboard Cite

The COVID‐19 pandemic is spreading at a fast pace around the world and has a high mortality rate. Since there is no proper treatment of COVID‐19 and its multiple variants, for example, Alpha, Beta, Gamma, and Delta, being more infectious in nature are affecting millions of people, further complicates the detection process, so, victims are at the risk of death. However, timely and accurate diagnosis of this deadly virus can not only save the patients from life loss but can also prevent them from the complex treatment procedures. Accurate segmentation and classification of COVID‐19 is a tedious job due to the extensive variations in its shape and similarity with other diseases like Pneumonia. Furthermore, the existing techniques have hardly focused on the infection growth estimation over time which can assist the doctors to better analyze the condition of COVID‐19‐affected patients. In this work, we tried to overcome the shortcomings of existing studies by proposing a model capable of segmenting, classifying the COVID‐19 from computed tomography images, and predicting its behavior over a certain period. The framework comprises four main steps: (i) data preparation, (ii) segmentation, (iii) infection growth estimation, and (iv) classification. After performing the pre‐processing step, we introduced the DenseNet‐77 based UNET approach. Initially, the DenseNet‐77 is used at the Encoder module of the UNET model to calculate the deep keypoints which are later segmented to show the coronavirus region. Then, the infection growth estimation of COVID‐19 per patient is estimated using the blob analysis. Finally, we employed the DenseNet‐77 framework as an end‐to‐end network to classify the input images into three classes namely healthy, COVID‐19‐affected, and pneumonia images. We evaluated the proposed model over the COVID‐19‐20 and COVIDx CT‐2A datasets for segmentation and classification tasks, respectively. Furthermore, unlike existing techniques, we performed a cross‐dataset evaluation to show the generalization ability of our method. The quantitative and qualitative evaluation confirms that our method is robust to both COVID‐19 segmentation and classification and can accurately predict the infection growth in a certain time frame.

show abstract

“…The test accuracy, sensitivity, and specificity of COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia, and healthy are 93.42%, 89.18%, and 98.92%, respectively. Loddo et al [17] proposed COVID-19 diagnosis from CT images using deep learning. Ten models were compared and VGG19 achieved the best accuracy of 98.87%.…”

Section: Related Workmentioning

confidence: 99%

Multi-Class Classification of Lung Diseases Using CNN Models

et al. 2021

View full text Add to dashboard Cite

In this study, we propose a multi-class classification method by learning lung disease images with Convolutional Neural Network (CNN). As the image data for learning, the U.S. National Institutes of Health (NIH) dataset divided into Normal, Pneumonia, and Pneumothorax and the Cheonan Soonchunhyang University Hospital dataset including Tuberculosis were used. To improve performance, preprocessing was performed with Center Crop while maintaining the aspect ratio of 1:1. As a Noisy Student of EfficientNet B7, fine-tuning learning was performed using the weights learned from ImageNet, and the features of each layer were maximally utilized using the Multi GAP structure. As a result of the experiment, Benchmarks measured with the NIH dataset showed the highest performance among the tested models with an accuracy of 85.32%, and the four-class predictions measured with data from Soonchunhyang University Hospital in Cheonan had an average accuracy of 96.1%, an average sensitivity of 92.2%, an average specificity of 97.4%, and an average inference time of 0.2 s.

show abstract

Deep Learning for COVID-19 Diagnosis from CT Images

Cited by 23 publications

References 74 publications

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare

COVID‐DAI: A novel framework forCOVID‐19 detection and infection growth estimation using computed tomography images

Multi-Class Classification of Lung Diseases Using CNN Models

Contact Info

Product

Resources

About