Detecting COVID-19 Pneumonia over Fuzzy Image Enhancement on Computed Tomography Images

Alzahrani, Ali; Bhuiyan, Md. Al-Amin; Akhter, Fahima

doi:10.1155/2022/1043299

Cited by 4 publications

(1 citation statement)

References 39 publications

(40 reference statements)

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“…Likewise, the use of models based on fuzzy logic, a theory initially proposed in [21], which deals with fuzzy sets to model uncertainties, has shown important results in several applications, including the medical area [22]. Regarding the use of CT images modeled by Fuzzy theory and Deep Learning algorithms, some studies have demonstrated its effectiveness when performed to classify images of COVID-19 [23].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Class-Expert Learning for Supporting Covid-19 Triage Based on Computed Tomography Data

Alvarenga¹,

Santos²,

Rodríguez³

et al. 2022

Learn. Nonlin. Mod.

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Deep learning applications in medical imaging have been achieving promising results in the detection of diseases, among which clinical trials in terms of screening and diagnosis of patients with COVID-19 stand out. Computed Tomography (CT) images of the chest have been used by specialists for the diagnosis of COVID-19. However, due to the need of the moment and the possibility of using computational resources to help the medical team, it is observed in the literature several proposed works using supervised learning, however it lacks unsupervised methods for the screening and diagnosis of patients with COVID-19. In this work, the deep learning models Convolutional Neural Network (CNN) and Variational Autoencoders are used for feature extraction and later this information is used for binary and multiclass classification in unsupervised methods (k-means, Fuzzy C-Means and Self-Organizing Maps). For this purpose, a public database containing 4173 CT images (2168 CT slices from COVID-19, 758 slices from Healthy and 1247 slices from other lung diseases) was used. The results show that feature extraction via Variational Autoencoders has similar performance with state-of-the-art models in the literature for COVID-19, mainly for the binary classification with accuracies of 95.9%, 92.1% and 95.9% for k-means, Fuzzy C-Means and SOM, respectively, presenting competitive results in the literature. It also shows the importance of extracting features through convolutional networks to improve classification performance, resulting from the use of deep learning and its state of the art in the area of computer vision.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Class-Expert Learning for Supporting Covid-19 Triage Based on Computed Tomography Data

Alvarenga¹,

Santos²,

Rodríguez³

et al. 2022

Learn. Nonlin. Mod.

View full text Add to dashboard Cite

show abstract

Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images

Al-Sheikh,

Al Dandan,

Al-Shamayleh

et al. 2023

Sci Rep

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Medical imaging is considered a suitable alternative testing method for the detection of lung diseases. Many researchers have been working to develop various detection methods that have aided in the prevention of lung diseases. To better understand the condition of the lung disease infection, chest X-Ray and CT scans are utilized to check the disease’s spread throughout the lungs. This study proposes an automated system for the detection multi lung diseases in X-Ray and CT scans. A customized convolutional neural network (CNN) and two pre-trained deep learning models with a new image enhancement model are proposed for image classification. The proposed lung disease detection comprises two main steps: pre-processing, and deep learning classification. The new image enhancement algorithm is developed in the pre-processing step using k-symbol Lerch transcendent functions model which enhancement images based on image pixel probability. While, in the classification step, the customized CNN architecture and two pre-trained CNN models Alex Net, and VGG16Net are developed. The proposed approach was tested on publicly available image datasets (CT, and X-Ray image dataset), and the results showed classification accuracy, sensitivity, and specificity of 98.60%, 98.40%, and 98.50% for the X-Ray image dataset, respectively, and 98.80%, 98.50%, 98.40% for the CT scans dataset, respectively. Overall, the obtained results highlight the advantages of the image enhancement model as a first step in processing.

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Experimental investigations of IoT connected Oximeter for Covid-19 infections

Balaji,

Sriram,

Saran

et al. 2023

Iv International Scientific Forum on Computer and Energy Sciences (Wfces Ii 2022)

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Detecting COVID-19 Pneumonia over Fuzzy Image Enhancement on Computed Tomography Images

Cited by 4 publications

References 39 publications

Unsupervised Class-Expert Learning for Supporting Covid-19 Triage Based on Computed Tomography Data

Unsupervised Class-Expert Learning for Supporting Covid-19 Triage Based on Computed Tomography Data

Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images

Experimental investigations of IoT connected Oximeter for Covid-19 infections

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