Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto‐Encoder Model

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dc.contributor.authorIsselmou, Abd El Kader
dc.contributor.authorGuizhi, Xu
dc.contributor.authorZhang, Shuai
dc.contributor.authorSaminu, Sani
dc.contributor.authorJavaid, Imran
dc.contributor.authorAhmad, Isah Salim
dc.contributor.authorKAMHI, SOUHA
dc.date.accessioned2022-01-10T11:33:34Z
dc.date.available2022-01-10T11:33:34Z
dc.date.issued2021-08-31
dc.description.abstractThe 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.en_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/7310
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectMRIen_US
dc.subjectbrain tumoren_US
dc.subjectdetectionen_US
dc.subjectclassificationen_US
dc.subjectseed growingen_US
dc.subjectsegmentationen_US
dc.subjectdeep wave‐ let auto‐encoderen_US
dc.titleBrain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto‐Encoder Modelen_US
dc.typeArticleen_US

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