Convolutional neural network for medical image classification using wavelet features

Amin Khatami*, Asef Nazari, Amin Beheshti, Thanh Thi Nguyen, Saeid Nahavandi, Zieba Jerzy

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Automatic classification algorithms are an important component of expert decision support systems that are used in a number of medical applications including diagnostic radiology and disease detection. This study proposes a deep learning-based framework for medical image classification using wavelet features. Convolutional neural networks are incorporated to discover informative latent patterns and features from a set of X-ray images pertaining to human body parts. The features are then passed to a classifier for labelling the respective X-ray images. The experimental results show that the low-pass filter wavelet-based convolutional model outperforms the original convolutional network and some models for classifying X-ray images. The performance of the proposed method implies that it can be implemented effectively in practice for disease detection using radiological images.
Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781728169262
DOIs
Publication statusPublished - 2020
Event2020 International Joint Conference on Neural Networks - Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2020 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2020
CountryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20

Keywords

  • Convolutional neural network
  • deep learning
  • classification
  • wavelet
  • medical imaging

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