Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Lorencin, Ivan; Šegota, Sandi Baressi; Anđelić, Nikola; Blagojević, Anđela; Šušteršič, Tijana; Protić, Alen; Arsenijević, Miloš; Ćabov, Tomislav; Filipović, Nenad; Car, Zlatan

doi:10.3390/jpm11010028

Cited by 24 publications

(14 citation statements)

References 69 publications

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“…Random forest was adopted to assemble a recognizable proof model between routine blood records and cellular breakdown in the lungs that would decide whether they were intensively connected. There are few recent studies have also used the Ai-driven approaches using Regression [42] and Classification methods to detect the COVID-19 and other lungs infections using time-series, pathological, CT, and X-ray data [43][44][45][46][47]. However, some of them are discussed.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

et al. 2021

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Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

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Section: Background and Literature Reviewmentioning

confidence: 99%

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

et al. 2021

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“…In addition to the convolution layers pooling layers are also used. These layers follow the application of a convolution layer and serve as an additional feature extractor, and they are used to lower the amount of data utilized inside the convolution layers [24].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Neural Network-Based Model for Classification of Faults During Operation of a Robotic Manipulator

et al. 2021

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The importance of error detection is high, especially in modern manufacturing processes where assembly lines operate without direct supervision. Stopping the faulty operation in time can prevent damage to the assembly line. Public dataset is used, containing 15 classes, 2 types of faultless operation and 13 types of faults, with 463 force and torsion datapoints. Four different methods are used: Multilayer Perceptron (MLP) selected due to high classification performance, Support Vector Machines (SVM) commonly used for a low number of datapoints, Convolutional Neural Network (CNN) known for high performance in classification with matrix inputs and Siamese Neural Network (SNN) novel method with high performance in small datasets. Two classification tasks are performed-error detection and classification. Grid search is used for hyperparameter variation and F1 score as a metric, with a 10 fold cross-validation. Authors propose a hybrid system consisting of SNN for detection and CNN for fault classification.

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“…The COVID-19 symptoms are variable, but generally include fever and cough [5][6][7]. However, people infected with COVID-19 may have different symptoms, and these symptoms may change over time.…”

Section: Introductionmentioning

confidence: 99%

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Anđelić

Šegota

Lorencin

et al. 2021

IJERPH

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Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22 January 2020–3 December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

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Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

Cited by 24 publications

References 69 publications

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

Neural Network-Based Model for Classification of Faults During Operation of a Robotic Manipulator

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

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