Asano, K., Kurose, A., Kamataki, A., et al. (2018) Importance and Accuracy of Intraoperative Frozen Section Diagnosis of the Resection Margin for Effective Carmustine Wafer Implantation. Brain Tumor Pathology, 35, 131-140.
https://doi.org/10.1007/s10014-018-0320-5

has been cited by the following article:

• TITLE: Brain Tumor Classification in Magnetic Resonance Images Using Deep Learning and Wavelet Transform

  AUTHORS: Ahmad M. Sarhan

  KEYWORDS: Convolutional Neural Network CNN), Wavelet Transform, Image Classification, Brain Cancer, Magnetic Resonance Imaging (MRI)

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.13 No.6, June 17, 2020

  ABSTRACT: A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign (noncancerous) or malignant (cancerous). Conventional diagnosis of a brain tumor by the radiologist is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologists reach their goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images. The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet Transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.3%.