Screening of COVID-19 Based on GLCM Features from CT Images Using Machine Learning Classifiers

Godbin, A. Beena; Jasmine, S. Graceline

doi:10.1007/s42979-022-01583-2

Cited by 10 publications

(12 citation statements)

References 21 publications

(16 reference statements)

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“…The ROA is an approach that determines the minimum points on the earth's surface that are impacted by the natural flow of rain droplets and precipitation occurrences (Godbin and Jasmine 2023 ). A raindrop can be used to represent each answer to the problem.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

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Ant Colony Optimization–Rain Optimization Algorithm Based on Hybrid Deep Learning for Diagnosis of Lung Involvement in Coronavirus Patients

Challab

Mardukhi

2023

Iran J Sci Technol Trans Electr Eng

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With today's rapid increase in population, automatic diagnosis of disease has emerged as a critical subject in the field of medicine. An automatic disease detection framework gives correct, accurate, and rapid outputs, supporting clinicians in making accurate diagnoses while also reducing the number of deaths from disease. This paper aims to develop a system for detecting anomalies, and to reach this objective, various concepts of machine learning, for example, support vector machines, are employed for the construction of a detection system with a deep learning algorithm, termed long short-term memory. Accordingly, the study proposes an approach for detecting lung involvement. A dataset consisting of 4575 CT scan pictures, of which 1525 were from the COVID-19 virus, was employed in the current investigation. The proposed system attained 99.9487% accuracy, 99.9485% specificity, 99.9485% sensitivity, and a 99.8787% F1-score based on experimental results. Based on the data provided, the system was able to accomplish the expected results. The experimental findings highlight the favorable features in improving classification accuracy and selecting the best attributes. The method thus shows promise as a convenient diagnostic tool to aid physicians in clinical decision-making.

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Section: Optimization Algorithmsmentioning

confidence: 99%

“…Godbin and Jasmine ( 2023 ) employed SVM, KNN, random forest, and XGBoost (eXtreme Gradient Boosting) classifiers in conjunction with light GBM (LGBM) to identify COVID patients' symptoms based on CT. Tuning tests were conducted to adjust the model's hyperparameters. In all, 2481 CT images were utilized in the dataset.…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization–Rain Optimization Algorithm Based on Hybrid Deep Learning for Diagnosis of Lung Involvement in Coronavirus Patients

Challab

Mardukhi

2023

Iran J Sci Technol Trans Electr Eng

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“…After image pre-processing feature extraction achieved using GLCM global descriptor, GLCM retrieved additional information from its layers to find the precise photos from the repository, their characteristics in terms of parameters such as distance, angle, resolution, level, and symmetric values. These values are retrieved from the pictures using the GLCM technique by image attributes such as Dissimilarity, Correlation, Homogeneity, Contrast, ASM, and Energy (1)(2)(3) . The photos are examined at several angles (0, 45, 90, and 135) to extract the characteristics using the layers.…”

Section: Additional Feature Extractionmentioning

confidence: 99%

“…With the rapid advancement in medical imaging and automatic diagnosis systems, plenty of medical images are being collected, stored in DICOM (Digital Imaging and Communications in Medicine) format. Many medical imaging devices such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and X-ray are used to acquire medical images, extremely important for diagnosis, detection, monitoring and treatment planning (2) . Deep learning-based approaches for content-based image retrieval (CBIR) of medical images are a dynamic field of research, but suffer from some significant limitations.…”

Section: Introductionmentioning

confidence: 99%

“…A variety of sets of images and datasets in existing techniques perform medical image retrieval such as, Computed tomography (CT) (1) , Chest CT images of COVID (2) , CE-MRI and OASIS dataset (8) , CE-MRI (10) , Diagnosis of Liver Tumour (18) , CRC-5000, CE-MRI, Diabetic Retinopathy (DR) (16) , NCT-CRC-HE-100 K (19) , X-ray images and Computerized Tomography (CT) scans, including pneumonia, tuberculosis, and lung cancer (20) etc., are used in the current approaches to perform medical image retrieval. The following are the primary contributions of this study extracting additional features in terms of parameters such as distance, angle, resolution, level, and symmetric values.…”

Section: Introductionmentioning

confidence: 99%

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An Image Classification and Retrieval Hybrid Model for Larger Healthcare Datasets using Deep Learning

Vasudeva,

Chandrashekara

2023

IJST

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Objectives:The objective of this work is to obtain an efficient medical image retrieval and classification from a larger healthcare datasets using Novel approach. Methods: In this study five different classes of Medical images are taken for input, features are extracted using GLCM (Grey Level Co-occurrence Matrix) by image attributes such as dissimilarity, correlation, homogeneity, contrast, ASM, and energy. The photos are examined at several angles (0, 45, 90, and 135) to extract the characteristics using the layers. The received feature vectors are input into the most often used deep learning models Artificial Neural Networks (ANN) and Convolution Neural Networks (CNN) for image classification. Then CNN model is integrated with a deep learning model based on Long-Short Term Memory (LSTM), which incorporates additional layers into its structure and works on large datasets. Further the retrieval performance is improved by Euclidean Distance Technique. Findings: Performance evaluation is performed by comparing and analyzing the experimental findings of proposed methods, ANN, CNN and CNN-LSTM yields the retrieval accuracy of 97.79%, 98.78% and 99.4%. The Precision, Recall and F1-Score are also compared, and they are more accurate when picture classification is performed on larger healthcare datasets. Novelty: The additional feature extraction using GLCM and the proposed hybrid model can extract better medical image features, and achieve higher classification accuracy compared with earlier image classification models.

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A Machine Learning Based Approach for Diagnosing Pneumonia with Boosting Techniques

Godbin,

Jasmine

2023

Studies in Computational Intelligence

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Screening of COVID-19 Based on GLCM Features from CT Images Using Machine Learning Classifiers

Cited by 10 publications

References 21 publications

Ant Colony Optimization–Rain Optimization Algorithm Based on Hybrid Deep Learning for Diagnosis of Lung Involvement in Coronavirus Patients

Ant Colony Optimization–Rain Optimization Algorithm Based on Hybrid Deep Learning for Diagnosis of Lung Involvement in Coronavirus Patients

An Image Classification and Retrieval Hybrid Model for Larger Healthcare Datasets using Deep Learning

A Machine Learning Based Approach for Diagnosing Pneumonia with Boosting Techniques

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