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Deep CNN for Brain Tumor Classification

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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.

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Authors and Affiliations

  1. Laboratory of Electronics and Microelectronics, University of Monastir, Monastir, Tunisia

    Wadhah Ayadi

  2. College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

    Wajdi Elhamzi

  3. Higher School of Sciences and Technology of Hammam Sousse, University of Sousse, Sousse, Tunisia

    Wajdi Elhamzi & Imen Charfi

  4. College of Computer Science, King Khalid University, Abha, Saudi Arabia

    Mohamed Atri

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  1. Wadhah Ayadi
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  2. Wajdi Elhamzi
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  4. Mohamed Atri
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Correspondence to Wajdi Elhamzi.

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Ayadi, W., Elhamzi, W., Charfi, I. et al. Deep CNN for Brain Tumor Classification. Neural Process Lett 53, 671–700 (2021). https://doi.org/10.1007/s11063-020-10398-2

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