The study and application of brain tumors classification based on artificial intelligence

Abstract

The application of AI in the medical field has been increasingly popular due to its ability to enhance the accuracy and efficiency of diagnoses. A plurality of individuals employs diverse techniques and refine existing methods in order to achieve greater precision. This paper provides an overview of the methodology utilized in early research papers for brain tumors classification. This includes the input of datasets, preprocessing, model building, training and testing, evaluation, and application. In addition, this paper presents four models used in early research, including the Artificial neural network (ANN) which mimics the organization and operation of neural networks in living organisms by processing information through interconnected nodes or neurons. Another model is k-Nearest Neighbor (KNN), an instance-based learning algorithm that labels new data points by comparing them to the K closest labeled data points in the training dataset. The third model is Visual Geometry Group-16 (VGG-16), a 16-layer Convolutional Neural Network (CNN) that is highly regarded for its simplicity and effectiveness and is one of the top-performing CNN models used by VGG. Lastly, GN-AlexNet is a hybrid learning mode that combines the GoogleNet structure with the AlexNet model. The remainder of the article explains the utilization of this AI in three application scenarios, namely as an assistant for doctors, patients, and insurance companies. Moreover, the paper highlights potential challenges that may arise in both the present and future, such as patient mistrust and the emergence of new models like Vision Transformer (ViT) that could potentially outperform CNNs.