A review of convolutional neural network based methods for medical image classification

This study systematically reviews CNN-based medical image classification methods. We surveyed 149 of the latest and most important papers published to date and conducted an in-depth analysis of the methods used therein. Based on the selected literature, we organized this review systematically. First, the development and evolution of CNN in the field of medical image classification are analyzed. Subsequently, we provide an in-depth overview of the main techniques of CNN applied to medical image classification, which is also the current research focus in this field, including data preprocessing, transfer learning, CNN architectures, and explainability, and their role in improving classification accuracy and efficiency. In addition, this overview summarizes the main public datasets for various diseases. Although CNN has great potential in medical image classification tasks and has achieved good results, clinical application is still difficult. Therefore, we conclude by discussing the main challenges faced by CNNs in medical image analysis and pointing out future research directions to address these challenges. This review will help researchers with their future studies and can promote the successful integration of deep learning into clinical practice and smart medical systems.