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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2021

06.01.2021

Deep CNN for Brain Tumor Classification

verfasst von: Wadhah Ayadi, Wajdi Elhamzi, Imen Charfi, Mohamed Atri

Erschienen in: Neural Processing Letters | Ausgabe 1/2021

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Abstract

Brain tumor represents one of the most fatal cancers around the world. It is common cancer in adults and children. It has the lowest survival rate and various types depending on their location, texture, and shape. The wrong classification of the tumor brain will lead to bad consequences. Consequently, identifying the correct type and grade of tumor in the early stages has an important role to choose a precise treatment plan. Examining the magnetic resonance imaging (MRI) images of the patient’s brain represents an effective technique to distinguish brain tumors. Due to the big amounts of data and the various brain tumor types, the manual technique becomes time-consuming and can lead to human errors. Therefore, an automated computer assisted diagnosis (CAD) system is required. The recent evolution in image classification techniques has shown great progress especially the deep convolution neural networks (CNNs) which have succeeded in this area. In this regard, we exploited CNN for the problem of brain tumor classification. We suggested a new model, which contains various layers in the aim to classify MRI brain tumor. The proposed model is experimentally evaluated on three datasets. Experimental results affirm that the suggested approach provides a convincing performance compared to existing methods.
Vorheriger Artikel Object Reconstruction Based on Attentive Recurrent Network from Single and Multiple Images
Nächster Artikel Deep Neural Networks Regularization Using a Combination of Sparsity Inducing Feature Selection Methods

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Metadaten
Titel
Deep CNN for Brain Tumor Classification
verfasst von
Wadhah Ayadi
Wajdi Elhamzi
Imen Charfi
Mohamed Atri
Publikationsdatum
06.01.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2021
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10398-2

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