Convolutional Neural Networks in Multi-Class Classification of Medical Data

• URL: http://arxiv.org/abs/2012.14059v1
• Date: Mon, 28 Dec 2020 02:04:38 GMT
• Title: Convolutional Neural Networks in Multi-Class Classification of Medical Data
• Authors: YuanZheng Hu, Marina Sokolova
• Abstract summary: We introduce an ensemble model that consists of both deep learning (CNN) and shallow learning models (Gradient Boosting) The method achieves Accuracy of 64.93, the highest three-class classification accuracy we achieved in this study.
• Score: 0.9137554315375922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We report applications of Convolutional Neural Networks (CNN) to multi-classification classification of a large medical data set. We discuss in detail how changes in the CNN model and the data pre-processing impact the classification results. In the end, we introduce an ensemble model that consists of both deep learning (CNN) and shallow learning models (Gradient Boosting). The method achieves Accuracy of 64.93, the highest three-class classification accuracy we achieved in this study. Our results also show that CNN and the ensemble consistently obtain a higher Recall than Precision. The highest Recall is 68.87, whereas the highest Precision is 65.04.

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