심장 소리 분류를 위한 Inverted Residuals 기반 경량화 모델

Abstract

For the treatment and prevention of heart diseases, which is the leading cause of high mortality rate globally, the need for healthcare devices equipped with artificial intelligence(AI) model that can monitor in real-time and analyze heart conditions is increasing. Therefore, in this study, we propose a light CNN that can be applied to healthcare devices, using the PASCAL data. The proposed model used MFCC feature extraction method suitable for heart sound range, The light CNN was designed with the inverted residuals used in MobileNetV2. The experiments showed that the proposed model with fewer 82.5% of the learnable parameters, achieved similar performance in accuracy within the range of 1 to 2% compared to the previous studies. It was confirmed that the proposed light CNN can be feasibly incorporated on mobile devices by means of comparative experiments in a reasonable amount of computation.