FINKŠT, Tomaž ;TASIČ, Jurij Franc;ZORMAN TERČELJ, Marjeta ;MEŽA, Marko . Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 63, n.12, p. 685-695, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/classification-of-malignancy-suspicious-lesions-in-autofluorescence-bronchoscopy/>. Date accessed: 19 apr. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2016.4019.
Finkšt, T., Tasič, J., Zorman Terčelj, M., & Meža, M. (2017). Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy. Strojniški vestnik - Journal of Mechanical Engineering, 63(12), 685-695. doi:http://dx.doi.org/10.5545/sv-jme.2016.4019
@article{sv-jmesv-jme.2016.4019,
author = {Tomaž Finkšt and Jurij Franc Tasič and Marjeta Zorman Terčelj and Marko Meža},
title = {Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {63},
number = {12},
year = {2017},
keywords = {image segmentation; edge detection; autofluorescence bronchoscopy; machine learning},
abstract = {This paper presents a novel approach to the classification of bronchial tissue as either malignant or precancerous based on autofluorescence bronchoscopy (AFB) images. The study consisted of 44 images, of which 22 were confirmed as malignant and 22 as nonmalignant precancerous cases. Our approach starts with the detection of a region of interest (ROI). This is followed by an analysis of semi-normal intensity distributions in gray-scale images of red and green components of the previously identified ROI. Based on the results of this analysis, features are computed, which are then used to build an image-classification model. This model classifies the tissue images into malignant/nonmalignant classes. We utilized several classification algorithms, i.e., naive Bayes, K-nearest-neighbor (K-NN), and support vector machine (SVM) with dot kernel. The criteria used when testing their performance were accuracy, sensitivity, specificity, and the area under the curve. Wilcoxon’s signed-rank test was used to confirm the accuracy of the classification method. The proposed method was compared to a similar approach reported by Buountris et al., who analyzed the texture features in a gray-level co-occurrence matrix (GLCM). Using the bestperforming classification algorithm (SVM with dot kernel), the accuracy of the proposed approach (95.8 %) was better than that reported by Bountris et al. (92.1 %).},
issn = {0039-2480}, pages = {685-695}, doi = {10.5545/sv-jme.2016.4019},
url = {https://www.sv-jme.eu/article/classification-of-malignancy-suspicious-lesions-in-autofluorescence-bronchoscopy/}
}
Finkšt, T.,Tasič, J.,Zorman Terčelj, M.,Meža, M. 2017 June 63. Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 63:12
%A Finkšt, Tomaž %A Tasič, Jurij Franc %A Zorman Terčelj, Marjeta %A Meža, Marko %D 2017 %T Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy %B 2017 %9 image segmentation; edge detection; autofluorescence bronchoscopy; machine learning %! Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy %K image segmentation; edge detection; autofluorescence bronchoscopy; machine learning %X This paper presents a novel approach to the classification of bronchial tissue as either malignant or precancerous based on autofluorescence bronchoscopy (AFB) images. The study consisted of 44 images, of which 22 were confirmed as malignant and 22 as nonmalignant precancerous cases. Our approach starts with the detection of a region of interest (ROI). This is followed by an analysis of semi-normal intensity distributions in gray-scale images of red and green components of the previously identified ROI. Based on the results of this analysis, features are computed, which are then used to build an image-classification model. This model classifies the tissue images into malignant/nonmalignant classes. We utilized several classification algorithms, i.e., naive Bayes, K-nearest-neighbor (K-NN), and support vector machine (SVM) with dot kernel. The criteria used when testing their performance were accuracy, sensitivity, specificity, and the area under the curve. Wilcoxon’s signed-rank test was used to confirm the accuracy of the classification method. The proposed method was compared to a similar approach reported by Buountris et al., who analyzed the texture features in a gray-level co-occurrence matrix (GLCM). Using the bestperforming classification algorithm (SVM with dot kernel), the accuracy of the proposed approach (95.8 %) was better than that reported by Bountris et al. (92.1 %). %U https://www.sv-jme.eu/article/classification-of-malignancy-suspicious-lesions-in-autofluorescence-bronchoscopy/ %0 Journal Article %R 10.5545/sv-jme.2016.4019 %& 685 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 63 %N 12 %@ 0039-2480 %8 2018-06-27 %7 2018-06-27
Finkšt, Tomaž, Jurij Franc Tasič, Marjeta Zorman Terčelj, & Marko Meža. "Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy." Strojniški vestnik - Journal of Mechanical Engineering [Online], 63.12 (2017): 685-695. Web. 19 Apr. 2024
TY - JOUR AU - Finkšt, Tomaž AU - Tasič, Jurij Franc AU - Zorman Terčelj, Marjeta AU - Meža, Marko PY - 2017 TI - Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2016.4019 KW - image segmentation; edge detection; autofluorescence bronchoscopy; machine learning N2 - This paper presents a novel approach to the classification of bronchial tissue as either malignant or precancerous based on autofluorescence bronchoscopy (AFB) images. The study consisted of 44 images, of which 22 were confirmed as malignant and 22 as nonmalignant precancerous cases. Our approach starts with the detection of a region of interest (ROI). This is followed by an analysis of semi-normal intensity distributions in gray-scale images of red and green components of the previously identified ROI. Based on the results of this analysis, features are computed, which are then used to build an image-classification model. This model classifies the tissue images into malignant/nonmalignant classes. We utilized several classification algorithms, i.e., naive Bayes, K-nearest-neighbor (K-NN), and support vector machine (SVM) with dot kernel. The criteria used when testing their performance were accuracy, sensitivity, specificity, and the area under the curve. Wilcoxon’s signed-rank test was used to confirm the accuracy of the classification method. The proposed method was compared to a similar approach reported by Buountris et al., who analyzed the texture features in a gray-level co-occurrence matrix (GLCM). Using the bestperforming classification algorithm (SVM with dot kernel), the accuracy of the proposed approach (95.8 %) was better than that reported by Bountris et al. (92.1 %). UR - https://www.sv-jme.eu/article/classification-of-malignancy-suspicious-lesions-in-autofluorescence-bronchoscopy/
@article{{sv-jme}{sv-jme.2016.4019},
author = {Finkšt, T., Tasič, J., Zorman Terčelj, M., Meža, M.},
title = {Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {63},
number = {12},
year = {2017},
doi = {10.5545/sv-jme.2016.4019},
url = {https://www.sv-jme.eu/article/classification-of-malignancy-suspicious-lesions-in-autofluorescence-bronchoscopy/}
}
TY - JOUR AU - Finkšt, Tomaž AU - Tasič, Jurij Franc AU - Zorman Terčelj, Marjeta AU - Meža, Marko PY - 2018/06/27 TI - Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 63, No 12 (2017): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2016.4019 KW - image segmentation, edge detection, autofluorescence bronchoscopy, machine learning N2 - This paper presents a novel approach to the classification of bronchial tissue as either malignant or precancerous based on autofluorescence bronchoscopy (AFB) images. The study consisted of 44 images, of which 22 were confirmed as malignant and 22 as nonmalignant precancerous cases. Our approach starts with the detection of a region of interest (ROI). This is followed by an analysis of semi-normal intensity distributions in gray-scale images of red and green components of the previously identified ROI. Based on the results of this analysis, features are computed, which are then used to build an image-classification model. This model classifies the tissue images into malignant/nonmalignant classes. We utilized several classification algorithms, i.e., naive Bayes, K-nearest-neighbor (K-NN), and support vector machine (SVM) with dot kernel. The criteria used when testing their performance were accuracy, sensitivity, specificity, and the area under the curve. Wilcoxon’s signed-rank test was used to confirm the accuracy of the classification method. The proposed method was compared to a similar approach reported by Buountris et al., who analyzed the texture features in a gray-level co-occurrence matrix (GLCM). Using the bestperforming classification algorithm (SVM with dot kernel), the accuracy of the proposed approach (95.8 %) was better than that reported by Bountris et al. (92.1 %). UR - https://www.sv-jme.eu/article/classification-of-malignancy-suspicious-lesions-in-autofluorescence-bronchoscopy/
Finkšt, Tomaž, Tasič, Jurij, Zorman Terčelj, Marjeta, AND Meža, Marko. "Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 63 Number 12 (27 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 63(2017)12, 685-695
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
This paper presents a novel approach to the classification of bronchial tissue as either malignant or precancerous based on autofluorescence bronchoscopy (AFB) images. The study consisted of 44 images, of which 22 were confirmed as malignant and 22 as nonmalignant precancerous cases. Our approach starts with the detection of a region of interest (ROI). This is followed by an analysis of semi-normal intensity distributions in gray-scale images of red and green components of the previously identified ROI. Based on the results of this analysis, features are computed, which are then used to build an image-classification model. This model classifies the tissue images into malignant/nonmalignant classes. We utilized several classification algorithms, i.e., naive Bayes, K-nearest-neighbor (K-NN), and support vector machine (SVM) with dot kernel. The criteria used when testing their performance were accuracy, sensitivity, specificity, and the area under the curve. Wilcoxon’s signed-rank test was used to confirm the accuracy of the classification method. The proposed method was compared to a similar approach reported by Buountris et al., who analyzed the texture features in a gray-level co-occurrence matrix (GLCM). Using the bestperforming classification algorithm (SVM with dot kernel), the accuracy of the proposed approach (95.8 %) was better than that reported by Bountris et al. (92.1 %).