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Hashiguchi, Yoshifumi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : Journal of Information Technology and Computer Science

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A Study on Sound Analysis Algorithm for Heart Sounds using YOLO Deep Learning Model Tamura, Hiroki; Gunaratne, Praveen Nuwantha; Takeguchi, Hiromu; Fukumoto, Hiroyuki; Hashiguchi, Yoshifumi
Journal of Information Technology and Computer Science Vol. 7 No. 2: August 2022
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.72443

Abstract

As Japan’s population ages, the demand for home medical care is increasing. And, as the demand for home medical care increases, the burden on medical personnel becomes a problem. One of the ways to promote home medical care is to spread the use of medical equipment in households. However, since some medical knowledge is required to use basic medical equipment, it is considered difficult to spread the use of medical equipment among general households. Therefore, it demands the necessity to develop a medical device that can give the same decision as a medical doctor by using an algorithm. In this paper, we study the construction of an algorithm for an AI stethoscope that can make the same decisions as a medical doctor. We combine a frequency analysis method using three features and an image processing method using an image that represents the frequency features by wavelet transform. Using the results of each of these methods, we aim to improve the identification rate through machine learning techniques. The Random Forest training yields an identification rate of 94.68 ％ on the dataset of this paper.
Co-Authors Fukumoto, Hiroyuki Gunaratne, Praveen Nuwantha Takeguchi, Hiromu Tamura, Hiroki