Convolutional Neural Networks in Multi-Class Classification of Medical Data
This work provides an incremental improvement in multi-class classification accuracy for medical data, which could benefit medical diagnostic applications.
This paper explores the application of Convolutional Neural Networks (CNNs) for multi-class classification on a large medical dataset, investigating the impact of model and data preprocessing changes. The study culminates in an ensemble model combining CNNs and Gradient Boosting, achieving a peak three-class classification accuracy of 64.93%.
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.