A New CNN-based Joint Network for Brain Tumor Denoising and Classification

The Deep Learning (DL) method, especially the Convolutional Neural Network (CNN) based method have been applied in various fields of medical image processing and proven to be powerful for image classification, denoising task. However, there are not any end-to-end DL models for both denoising and classification tasks in existing methods, which would render a large number of parameters for models to deal with these tasks. In this work, we proposed an efficient auto-encoder model based on CNN for classification and denoising tasks for brain medical images (i.e. Magnetic Resonance Imaging), in addition, we proposed a connection between encoding and decoding units in our auto-encoder model and evaluated the contribution of it to denoising task. Our method achieved 99.7% in accuracy of classification the brain medical images on the Brain Tumor dataset from Kaggle, which has 3,762 brain medical images, in addition, the denoising performance of it was impressive, as the lowest average Mean Square Error (MSE) of the dataset was 0.00503 while the model achieved best classification performance.