Land Cover Classification from Satellite Data using Machine Learning Techniques

Vora, Nisarg; Patel, Arush; Shah, Kathan; Saikia, Pallabi

doi:10.1109/aimv53313.2021.9671016

“…Similar to LC detection, the use of CAD in COPD detection has gained significant attention in recent years. According to [125], early detection and classification of illness can significantly increase the possibility of patient relief. In the clinical field, AI and expert systems have been successfully employed to reduce the time required for diagnosis and classification and to enhance the accuracy of the results.…”

Section: B Copd Detectionmentioning

confidence: 99%

Diagnosing Lung Pathologies: A Systematic Survey, Current Trends, and Future Orientation

Al-Hasan¹,

Noorizadeh²,

Bensaali³

et al. 2023

Preprint

0

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<p>This study offers a comparative analysis of different diagnostic techniques for lung pathologies from an engineering standpoint. The review concentrates on three primary categories of methods: electronic nose-based (E-nose) detection, computer vision or image processing, and biosensors such as Graphene-FET (GFET). The e-nose-based detection technique uses electronic sensors to recognize Volatile Organic Compounds (VOCs) in the exhaled breath. These VOCs can aid in the diagnosis of lung pathologies such as pneumonia. The computer vision processing method involves the application of advanced imaging techniques and Machine Learning algorithms to scrutinize and diagnose lung pathologies and Ventilator-Associated Pneumonia (VAP). Lastly, biosensors employ the exceptional properties of these materials to identify specific biomarkers in biological samples. This information can be used to diagnose lung pathologies and VAP. The present paper examines the current state-of-the-art in each methods and offers a comprehensive analysis of their advantages and disadvantages from an engineering perspective. The study underscores the potential of these techniques to enhance the diagnosis of lung pathologies and VAP. Additionally, it emphasizes the necessity for further research to optimize their performance and clinical usefulness.</p>

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“…It is particularly well-suited for classification problems by finding the optimal hyperplane. SVM is widely used in various fields which can be applied in diagnosing COPD and classifying its severity [25,26].…”

Section: Introductionmentioning

confidence: 99%

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images

Zhao,

Wu,

Li

et al. 2024

BMC Pulm Med

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Background Chronic obstructive pulmonary disease (COPD) is a prevalent and debilitating respiratory condition that imposes a significant healthcare burden worldwide. Accurate staging of COPD severity is crucial for patient management and treatment planning. Methods The retrospective study included 530 hospital patients. A lobe-based radiomics method was proposed to classify COPD severity using computed tomography (CT) images. First, we segmented the lung lobes with a convolutional neural network model. Secondly, the radiomic features of each lung lobe are extracted from CT images, the features of the five lung lobes are merged, and the selection of features is accomplished through the utilization of a variance threshold, t-Test, least absolute shrinkage and selection operator (LASSO). Finally, the COPD severity was classified by a support vector machine (SVM) classifier. Results 104 features were selected for staging COPD according to the Global initiative for chronic Obstructive Lung Disease (GOLD). The SVM classifier showed remarkable performance with an accuracy of 0.63. Moreover, an additional set of 132 features were selected to distinguish between milder (GOLD I + GOLD II) and more severe instances (GOLD III + GOLD IV) of COPD. The accuracy for SVM stood at 0.87. Conclusions The proposed method proved that the novel lobe-based radiomics method can significantly contribute to the refinement of COPD severity staging. By combining radiomic features from each lung lobe, it can obtain a more comprehensive and rich set of features and better capture the CT radiomic features of the lung than simply observing the lung as a whole.

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Diagnosing Lung Pathologies: A Systematic Survey, Current Trends, and Future Orientation

Al-Hasan¹,

Noorizadeh²,

Bensaali³

et al. 2023

Preprint

0

View full text Add to dashboard Cite

<p>This study offers a comparative analysis of different diagnostic techniques for lung pathologies from an engineering standpoint. The review concentrates on three primary categories of methods: electronic nose-based (E-nose) detection, computer vision or image processing, and biosensors such as Graphene-FET (GFET). The e-nose-based detection technique uses electronic sensors to recognize Volatile Organic Compounds (VOCs) in the exhaled breath. These VOCs can aid in the diagnosis of lung pathologies such as pneumonia. The computer vision processing method involves the application of advanced imaging techniques and Machine Learning algorithms to scrutinize and diagnose lung pathologies and Ventilator-Associated Pneumonia (VAP). Lastly, biosensors employ the exceptional properties of these materials to identify specific biomarkers in biological samples. This information can be used to diagnose lung pathologies and VAP. The present paper examines the current state-of-the-art in each methods and offers a comprehensive analysis of their advantages and disadvantages from an engineering perspective. The study underscores the potential of these techniques to enhance the diagnosis of lung pathologies and VAP. Additionally, it emphasizes the necessity for further research to optimize their performance and clinical usefulness.</p>

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Land Cover Classification from Satellite Data using Machine Learning Techniques

Cited by 3 publications

References 14 publications

Diagnosing Lung Pathologies: A Systematic Survey, Current Trends, and Future Orientation

Diagnosing Lung Pathologies: A Systematic Survey, Current Trends, and Future Orientation

Learning and depicting lobe-based radiomics feature for COPD Severity staging in low-dose CT images

Diagnosing Lung Pathologies: A Systematic Survey, Current Trends, and Future Orientation

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