Comparison and analysis of the number of convolutional layers in CNN-based breast cancer detection

Abstract

Breast cancer is becoming more and more common, and the mortality rate is increasing, which is indeed a serious problem. To alleviate this problem, this study applies a convolutional neural network (CNN) model to analyze breast cancer images formed by mammography. The CNN model has the property of automatically learning hierarchical feature representations from the original image data. The structure of the CNN enables it to capture spatially hierarchical features at multiple scales, including edges, textures, and objects. Specifically, this study explores the performance of CNN models on prediction tasks by varying the number of convolutional layers. This study is conducted on the DDSM mammography dataset. The experimental results demonstrate that CNN models are effective for detecting breast cancer, both in terms of accuracy and loss rate, and that more convolutional layers improve performance. In particular, the model saturates at four convolutional layers to reach the highest performance. Thus, this study helps to accelerate the efficiency of breast cancer detection and paves the way for more efficient methods in the future.