Title: Spotting congenital heart diseases using palm print based on faster R-CNN and spatial method

Authors: Y. Mahesha; C. Nagaraju

Addresses: The National Institute of Engineering, Visvesvaraya Technological University, Mysore, Karnataka, India ' The National Institute of Engineering, Visvesvaraya Technological University, Mysore, Karnataka, India

Abstract: This paper proposes a machine learning method to detect congenital heart diseases (CHDs) using a palm pattern known as axial triradius. This article spreads light on three things. First, Faster R-CNN Inception v2 has been used to identify triradii on the palm image. Secondly, a novel spatial method has been proposed to select leftmost, rightmost and axial triradii. Finally, the angle at axial triradius has been calculated on the palm images of healthy people and of patients suffering from tetralogy of Fallot (TOF), atrial septal defect (ASD), ventricular septal defect (VSD) and coarctation of aorta (CoA). The result shows that the proposed method can be used as a screening method to predict CHDs.

Keywords: axial triradius; CHDs; faster R-CNN; inception v2; spatial method.

DOI: 10.1504/IJMEI.2024.135685

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.1, pp.56 - 70

Received: 22 Jun 2021
Accepted: 07 Nov 2021
Published online: 22 Dec 2023 *