Study on the Effect of Depth on Convolutional Neural Networks for Brain Tumor Detection

Abstract

Brain tumors are one of the invasive diseases in children and adults. Due to the complexity involved in brain tumors and their characteristics, manual examination may be error prone. The use of automatic classification technology using machine learning (ML) has always shown higher accuracy than manual classification [1]. Convolutional Neural Network (CNN) is widely used as a deep learning algorithm in detecting brain tumor. However, many people have not paid their attention to how the number of layers affect the accuracy of CNN when applying it in detecting tumor. To find the relationship between the number of layers of CNN model detecting the brain tumor and the accuracy of the model, this study implements three different models with different number of layers. By using a certain data set and preprocessing, the three models' codes are successfully run, and figures and output of the accuracy and loss are showed up. After comparing the graphs and output, a certain relationship is concluded. In this article, it is indicated that the number of layers of model has positive relationship with the accuracy of this model: the more layers the model has, the higher accuracy this model will provide.