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Convolutional Neural Network with Batch Normalization for Classification of Endoscopic Gastrointestinal Diseases

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Dalia Ezzat
Heba M. Afify
Mohamed Hamed N. Taha
Aboul Ella Hassanien
Published in
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges
Publisher
Springer International Publishing

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Abstract

In this paper, an approach for classifying gastrointestinal (GI) diseases from endoscopic images is proposed. The proposed approach is built using a convolutional neural network (CNN) with batch normalization (BN) and an exponential linear unit (ELU) as the activation function. The proposed approach consists of eight layers (six convolutional and two fully connected layers) and is used to identify eight types of GI diseases in version two of the Kvasir dataset. The proposed approach was compared with other CNN architectures (VGG16, VGG19, and Inception-v3) using five elements (number of convolutional layers, number of total parameters of the convolutional layers, number of epochs, validation accuracy and test accuracy). The proposed approach achieved good results compared to the compared architectures. It achieved a validation accuracy of 88%, which is superior to other architectures and a test accuracy of 87%, which outperforms the Inception-v3 architecture. Therefore, the proposed approach has less trained images and less computational complexity in the training phase.

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previous chapter SQL Injection Attacks Detection and Prevention Based on Neuro—Fuzzy Technique
next chapter A Chaotic Search-Enhanced Genetic Algorithm for Bilevel Programming Problems
Title
Convolutional Neural Network with Batch Normalization for Classification of Endoscopic Gastrointestinal Diseases
Authors
Dalia Ezzat
Heba M. Afify
Mohamed Hamed N. Taha
Aboul Ella Hassanien
Copyright Year
2021
Book
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Print ISBN: 978-3-030-59337-7

Electronic ISBN: 978-3-030-59338-4

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-59338-4_7
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