Diagnosing brain tumors based on Edge-Impulse platform

Abstract

Brain tumors pose a serious threat to human health, and timely diagnosis is crucial for effective treatment. Conventional methods for diagnosing brain tumors, such as manual diagnosis, are often inaccurate and comparatively expensive. In this paper, a new method of diagnosis that can replace classic methods in the future is proposed. Ideas of machine learning is used to train a model so that it can classify images of brains into 2 categories: with tumors and with no tumors. Process of training is based on Edge Impulse, an AI platform on which model can be created conveniently. Technology used in the training is Convolutional Neural Network, which uses convolution as a mathematical operation instead of traditional matrix multiplication. It comprises convolutional layers, pooling layers and fully connected layers. Convolutional layers and pooling layers are used to operate feature maps, transforming them into a 1-dimensional matrix, followed by being multiplied by matrices of weights in fully connected layers. Cross entropy loss function is utilised to assess accuracy and modify weights. Transfer learning is also used to improve effectiveness and efficiency of training. Additionally, experiments are taken to determine the best image size suiting the model. This is accomplished by changing sizes of input image while keeping other parameters constant. Results of the experiments indicate that the model has a fantastic performance on brain tumor diagnosis.