Convolutional Neural Networks Based on Raspberry Pi for a Prototype of Vocal Cord Abnormalities Identification

This study aims to make a device prototype for identifying vocal cord abnormalities based on Raspberry Pi. This prototype could classify the abnormalities into seven classes, i.e., cysts, granulomas, nodules, normal, papilloma, paralysis, and no vocal cords. The applied method to classify is a deep learning algorithm, mainly using Convolutional Neural Network (CNN). In building the CNN model, we used a statistical method to form a model training scenario, also modified the AlexNet architecture model by optimizing the parameters. The optimized parameters in the test scenario obtained 95.35% accuracy. The CNN model implemented on the Raspberry Pi, and the test results obtained 79.75% accuracy.This study aims to make a device prototype for identifying vocal cord abnormalities based on Raspberry Pi. This prototype could classify the abnormalities into seven classes, i.e., cysts, granulomas, nodules, normal, papilloma, paralysis, and no vocal cords. The applied method to classify is a deep learning algorithm, mainly using Convolutional Neural Network (CNN). In building the CNN model, we used a statistical method to form a model training scenario, also modified the AlexNet architecture model by optimizing the parameters. The optimized parameters in the test scenario obtained 95.35% accuracy. The CNN model implemented on the Raspberry Pi, and the test results obtained 79.75% accurac