Brain tumor classification using deep convolutional autoencoder-based neural network: multi-task approach


Diagnosis, detection and classification of tumors, in the brain MRI images, are important because misdiagnosis can lead to death. This paper proposes a method that can diagnose brain tumors in the MRI images and classify them into 5 categories using a Convolutional Neural Network (CNN). The proposed network uses a Convolutional Auto-Encoder Neural Network (CANN) to extract and learn deep features of input images. Extracted deep features from each level are combined to make desirable features and improve results. To classify brain tumor into three categories (Meningioma, Glioma, and Pituitary) the proposed method was applied on Cheng dataset and has reached a considerable performance accuracy of 99.3%. To diagnosis and grading Glioma tumors, the proposed method was applied on IXI and BraTS 2017 datasets, and to classify brain images into six classes including Meningioma, Pituitary, Astrocytoma, High-Grade Glioma, Low-Grade Glioma and Normal images (No tumor), the all datasets including IXI, BraTS2017, Cheng and Hazrat-e-Rassol, was used by the proposed network, and it has reached desirable performance accuracy of 99.1% and 98.5%, respectively.

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We would like to thank Mr. Vahid Vahidpour for his valuable help and comments in this research project.

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Correspondence to Hassan Khotanlou.

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Bashir-Gonbadi, F., Khotanlou, H. Brain tumor classification using deep convolutional autoencoder-based neural network: multi-task approach. Multimed Tools Appl (2021).

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  • Auto-encoder
  • Brain tumor
  • Deep learning
  • Convolutional neural network