Identification of female genital tuberculosis in infertility using textural features
by Varsha Garg; Anita Sahoo; Vikas Saxena
International Journal of Swarm Intelligence (IJSI), Vol. 6, No. 2, 2021

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%.

Online publication date: Fri, 29-Oct-2021

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