Article: Identification of female genital tuberculosis in infertility using textural features Journal: International Journal of Swarm Intelligence (IJSI) 2021 Vol.6 No.2 pp.106 - 117 Abstract: The effect of female genital tuberculosis (FGTB) on fertility of women is a topic of discussion in the medical fraternity but has still not made inroads to computer-aided detection. The travails of an infertile woman could be alleviated if a non-invasive method such as transvaginal ultrasound (TVUS) could provide early insights to FGTB detection. In this paper, a novel effort has been made towards effective classification of FGTB as normal and abnormal in infertility using TVUS image analysis. Real-time TVUS images of female visiting for infertility treatments have been collected from medical centres in consultation with medical experts in India. The identification of FGTB in infertility is done in four stages: image augmentation, grey level co-occurrence matrix-based textural feature extraction, two-phased feature selection based on mutual information-based ranking followed by sequential forward selection, and classification. Experiments were conducted with different classifiers, in which maximum accuracy is obtained by support vector machine (SVM). The testing results show that SVM effectively classifies the dataset in hand showing a mean accuracy of 83.41%. The two-phased feature selection method is able to reduce the dimensionality of textural feature vectors by 76.19%. Inderscience Publishers - linking academia, business and industry through research

Title: Identification of female genital tuberculosis in infertility using textural features

Authors: Varsha Garg; Anita Sahoo; Vikas Saxena

Addresses: Jaypee Institute of Information Technology, Noida, India ' Jaypee Institute of Information Technology, Noida, India ' Jaypee Institute of Information Technology, Noida, India

Abstract: The effect of female genital tuberculosis (FGTB) on fertility of women is a topic of discussion in the medical fraternity but has still not made inroads to computer-aided detection. The travails of an infertile woman could be alleviated if a non-invasive method such as transvaginal ultrasound (TVUS) could provide early insights to FGTB detection. In this paper, a novel effort has been made towards effective classification of FGTB as normal and abnormal in infertility using TVUS image analysis. Real-time TVUS images of female visiting for infertility treatments have been collected from medical centres in consultation with medical experts in India. The identification of FGTB in infertility is done in four stages: image augmentation, grey level co-occurrence matrix-based textural feature extraction, two-phased feature selection based on mutual information-based ranking followed by sequential forward selection, and classification. Experiments were conducted with different classifiers, in which maximum accuracy is obtained by support vector machine (SVM). The testing results show that SVM effectively classifies the dataset in hand showing a mean accuracy of 83.41%. The two-phased feature selection method is able to reduce the dimensionality of textural feature vectors by 76.19%.

Keywords: genital tuberculosis; ultrasound image processing; textural feature; feature selection; mutual information; classification.

DOI: 10.1504/IJSI.2021.118609

International Journal of Swarm Intelligence, 2021 Vol.6 No.2, pp.106 - 117

Received: 16 Jun 2020
Accepted: 27 Nov 2020
Published online: 29 Oct 2021 *

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Title: Identification of female genital tuberculosis in infertility using textural features

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