Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto‐Encoder Model
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Date
2021-08-31
Authors
Isselmou, Abd El Kader
Guizhi, Xu
Zhang, Shuai
Saminu, Sani
Javaid, Imran
Ahmad, Isah Salim
KAMHI, SOUHA
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
The process of diagnosing brain tumors is very complicated for many reasons, including
the brain’s synaptic structure, size, and shape. Machine learning techniques are employed to help
doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques
have made a great achievement in medical image analysis. This paper proposed a deep wavelet
autoencoder model named “DWAE model”, employed to divide input data slice as a tumor (abnor‐
mal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI
images and their integration with the input images. A high median filter was utilized to merge slices.
We improved the output slices’ quality through highlight edges and smoothened input MR brain
images. Then, we applied the seed growing method based on 4‐connected since the thresholding
cluster equal pixels with input MR data. The segmented MR image slices provide two two‐layer
using the proposed deep wavelet auto‐encoder model. We then used 200 hidden units in the first
layer and 400 hidden units in the second layer. The softmax layer testing and training are performed
for the identification of the MR image normal and abnormal. The contribution of the deep wavelet
auto‐encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect
and classify the tumor with high accuracy, short time, and low loss validation. To train and test the
overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012,
BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and ab‐
normal images. The experiments results show that the proposed model achieved an accuracy of
99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed
DWAE model can facilitate the automatic detection of brain tumors.
Description
Keywords
MRI, brain tumor, detection, classification, seed growing, segmentation, deep wave‐ let auto‐encoder