Towards an Improved CNN Architecture for Brain Tumor Classification

  • Hajji TarikEmail author
  • Masrour Tawfik
  • Douzi Youssef
  • Serrhini Simohammed
  • Ouazzani Jamil Mohammed
  • Jaara El Miloud
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)


Machine learning (a subset of Artificial Intelligence) automatically creates analytic models that adapt to what they find in the data. Over time, the algorithm “learns” how to deliver more accurate results, whether the goal is to make smarter credit decisions, retail offers, medical diagnoses or fraud detection. The use of Deep Learning technology as a new Machine Learning tools has had considerable success in Digital Image Processing over the past few years. It has been widely used in several of complex problems and has proven to be a powerful solving tool. In this paper we present a comparative study between the famous convolutional architectures (LeNet, AlexNet, ZF Net, GoogLeNet, VGGNet, ResNets, DenseNet) and the Convolutional Neural Networks AsilNet that we propose concerning the classification of Brain Tumors (Aneurysms, Multiple Sclerosis, Hydrocephalus, Stroke, Infections, Cysts, Swelling, Hemorrhage, Bleeding, Inflammation) detected in the Magnetic Resonance Imaging.


Artificial Intelligence Machine Learning Deep Learning Magnetic Resonance Imaging Brain tumors Classification 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Hajji Tarik
    • 1
    Email author
  • Masrour Tawfik
    • 2
  • Douzi Youssef
    • 3
  • Serrhini Simohammed
    • 4
  • Ouazzani Jamil Mohammed
    • 1
  • Jaara El Miloud
    • 4
  1. 1.Laboratoire des Systèmes et Environnements Durables (SED)Université Privée de Fès (UPF)FèsMorocco
  2. 2.Artificial Intelligence for Engineering Sciences Team, National School of Arts and CraftsMoulay Ismail UniversityMeknesMorocco
  3. 3.Laboratoire d’Arithmétique, Calcul Scientifique et Applications (ACSA-FSO), Faculté des Sciences d’Oujda (FSO)Université Mohammed Premier (UMP)OujdaMorocco
  4. 4.Laboratoire de recherche en informatique (LARi), Faculté Des Sciences D’Oujda (FSO)Universite Mohammed Premier (UMP)OujdaMorocco

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