A Review on Texture Feature Analysis of Chest Computed Tomography Images for Detection and Classification of Pulmonary Diseases

Sawant, Pratik; Sreemathy, R.

doi:10.1007/978-981-99-2100-3_36

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Published studies suggest that shape and texture features characterize the most prevalent patterns related to lung abnormalities. Empirical studies indicate better performance of second and higher order statistical approaches as compared to the first order statistical features [28]. As texture parameters indicate variations in pixel intensity values of chest CT images, we propose a hybrid texture feature extraction approach followed by an efficient feature reduction technique to select the most relevant features to distinguish between COVID-19 and non-COVID-19 images.…”

Section: Related Workmentioning

confidence: 99%

Rapid Navigation Optimization - based Deep Convolutional Neural Network for Covid-19 Detection using CT Scans

Sawant,

Sreemathy

2024

IJCDS

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In December 2019 a highly infectious virus named 'Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) sparked a global pandemic. Deep Neural Networks have been extensively used to develop intelligent systems for accurate and timely diagnosis of COVID-19 infection using chest Computerized Tomography. However, Deep Learning approaches require a large annotated dataset. The fundamental goal of this research is to develop a model that would learn efficiently from a size-limited dataset. This study proposes a hybrid feature extraction approach. Our proposed technique exploits the CT imaging characteristics of COVID-19 infection through hand-crafted texture features and complex features extracted by a pre-trained ResNet101 network. The 7-layered Deep Convolutional Neural Network used for classification is optimized using a revolutionary rapid navigation optimization technique. The proposed optimization improves the Moth-Flame Optimizer by integrating the concept of Mayfly velocity to update the position of the Moth fly in the exploration space. When tested on an open access dataset, COVID -CT containing 349 COVID-19 positive CT images and 397 COVID-19 negative CT images, the accuracy, sensitivity, and specificity of the proposed rapid navigation optimization-based deep CNN classifier were 97.260%, 94.301%, and 99%, respectively. The proposed model was also tested on an augmented COVID -CT dataset and a larger dataset, COVIDx-CT-3A. The proposed model has exhibited an accuracy of 99.61% and 89.35% on the augmented COVID -CT dataset and COVIDx-CT-3A, respectively. Our proposed method outperformed the other published cutting-edge research works that have tested on the small COVID -CT dataset.

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Section: Related Workmentioning

confidence: 99%

Rapid Navigation Optimization - based Deep Convolutional Neural Network for Covid-19 Detection using CT Scans

Sawant,

Sreemathy

2024

IJCDS

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“…The review paper by Sawant & Sreemathy, (2023) [29] demonstrates the SVM model's potential for classifying lung cancer using CT scan images by combining texture features. Using CT scan images, Chia Ming et al (2018) [30] developed a model for lung disease classification that has higher accuracy by combining GLCM texture features with deep learning.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Non-Invasive Approach to Identify and Classify COVID-19 from CT Scan Images using Machine Learning in Combination with Hybrid Texture Features

kumar

2024

Preprint

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Rapid and accurate identification of COVID-19 and also other associated diseases is now crucial to limiting the disease's transmission, relaxing lockdown laws, and reducing the burden on public health infrastructures. Recently, several approaches and techniques have been proposed to identify the SARS-CoV-2 virus (COVID-19) using different clinical data and medical pictures. There are some limitations and shortcomings with the COVID-19 detection technologies that are currently available on the market. Because of this, it becomes essential to develop and study new diagnostic tools that have higher diagnostic accuracy while avoiding the shortcomings of existing tools. This study used the SARS-CoV-2 CT scan dataset to test non-linear SVM and Twin-SVM (TWSVM) classifiers in addition to textural characteristics such as GLCM, GLRLM, and ILMFD separately. There are a total of 2482 CT scan images in this database; 1252 of the scans show positive signs of SARS-CoV-2 infection (COVID-19), and 1230 show negative signs. Eight different models were developed in this work for the purpose of classifying and predicting COVID-19. We found that the GLCM + NLSVM model using RBF kernal, GLCM + TWSVM using linear kernal, GLRLM + NLSVM using RBF kernal, GLRLM + TWSVM using sigmoid, ILMFD + NLSVM using RBF kernal, ILMFD + TWSVM using polynomial kernal, Hybrid feature + NLSVM, and Hybrid feature + TWSVM all performed better in terms of evaluation done by performance metrics used in this work. For the given dataset, the Hybrid feature + NLSVM model with Linear Kernal yielded significantly better results out of eight models tested, including 100% accuracy, 100% recall, 100% precision, 100% F1-score, R-Squared = 1, and RMSE = 0. As a result, the high accuracy of this type of computer-aided screening method would significantly boost the speed and accuracy of COVID-19 diagnosis also encourage the study of other associated diseases with CT-scan images.

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A noval approach of classification of COVID-19 from chest CT-Scan images using ensemble classifier in combination with cognition based texture features

Kumar

2024

Multimed Tools Appl

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A Review on Texture Feature Analysis of Chest Computed Tomography Images for Detection and Classification of Pulmonary Diseases

Cited by 3 publications

References 26 publications

Rapid Navigation Optimization - based Deep Convolutional Neural Network for Covid-19 Detection using CT Scans

Rapid Navigation Optimization - based Deep Convolutional Neural Network for Covid-19 Detection using CT Scans

An Efficient and Non-Invasive Approach to Identify and Classify COVID-19 from CT Scan Images using Machine Learning in Combination with Hybrid Texture Features

A noval approach of classification of COVID-19 from chest CT-Scan images using ensemble classifier in combination with cognition based texture features

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