Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

Maksum, Vivin Umrotul M.; Novitasari, Dian Candra Rini; Hamid, Abdulloh

doi:10.15642/mantik.2021.7.1.74-85

Cited by 2 publications

(2 citation statements)

References 28 publications

(33 reference statements)

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“…The test shows that the ELM algorithm has an accuracy of 97% using 11 features and 91% using 5 features. The next research is research on the application of X-Ray image algorithms for COVID-19 detection [15]. This research results produce the best accuracy of 91.21%.…”

Section: Introductionmentioning

confidence: 97%

Identification of Resistor Types Using Extreme Learning Machine Algorithms and Morphological Operation

Nuraini

2022

ijics

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Electronic components are the basic elements to form a series of electronic devices that are usually used in everyday life. For someone who studies the field of electricity, knowledge of electrical components is an important thing. One of the components whose use is most often found in electronic circuits is a resistor. However, some people do not know about these types of resistors. Especially for someone or a student who will learn about electronic components. This study aims to develop an image processing system that can identify transistor type images using the Extreme Learning Machine (ELM) algorithm. This algorithm performs integrated learning through a special form of feedforward perceptron which has one hidden layer. In order for the ELM algorithm to work properly, information about the features contained in the object to be identified is needed. So, in this study the ELM algorithm is combined with morphological characteristics through parameters such as area, perimeter, eccentricity, major axis length, and minor axis length. Based on these parameters, features will be obtained which will be input in the identification process. At the evaluation stage, the precision value was 87%, recall was 84.47% and accuracy was 85.5%.

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Section: Introductionmentioning

confidence: 97%

Identification of Resistor Types Using Extreme Learning Machine Algorithms and Morphological Operation

Nuraini

2022

ijics

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“…This research shows that CovidNet outperforms other deep learning models in detecting COVID-19. Shorfuzzaman et al [2] proposes learning based on convolution neural network (CNN) by utilizing transfer learning using parameters (weights) from different models, then combining them into one model by extracting features from each image, Maksum et al [10] concluded that using the computer-aided diagnosis system can be used to classify chest X-ray images using the machine learning method. The initial stage is to do the preprocessing step using gray-level co-occurrence matrix (GLCM).…”

Section: Introductionmentioning

confidence: 99%

Combination of gray level co-occurrence matrix and artificial neural networks for classification of COVID-19 based on chest X-ray images

Imran,

Delsi Samsumar,

Subki

et al. 2024

IJ-AI

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This research uses the gray level co-occurrence matrix (GLCM) and artificial neural networks to classify COVID-19 images based on chest X-ray images. According to previous studies, there has never been a researcher who has integrated GLCM with artificial neural networks. Epochs 10, 30, 50, 70, 100, and 120 were used in this research. The total number of data points used in this investigation was 600, divided into 300 normal chests and 300 COVID-19 data points. Epoch 10 had 91% accuracy, epoch 30 had 91% accuracy, epoch 50 had 92% accuracy, epoch 70 had 91% accuracy, epoch 100 had 92% accuracy, and epoch 120 had 90% accuracy in categorization. As indicated by the results of the classification tests, combining GLCM and artificial neural networks can produce good results; a combination of these methods can yield a classification for COVID-19.

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Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

Cited by 2 publications

References 28 publications

Identification of Resistor Types Using Extreme Learning Machine Algorithms and Morphological Operation

Identification of Resistor Types Using Extreme Learning Machine Algorithms and Morphological Operation

Combination of gray level co-occurrence matrix and artificial neural networks for classification of COVID-19 based on chest X-ray images

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