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Convolutional neural network for medical image classification using wavelet features

conference contribution
posted on 2020-07-01, 00:00 authored by Amin Khatami, Asef NazariAsef Nazari, Amin Beheshti, Thanh Thi Nguyen, Saeid Nahavandi, Jerzy Zieba
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.

History

Pagination

1-8

Location

Online from Glasgow, Scotland

Start date

2020-07-19

End date

2020-07-24

ISBN-13

978-1-7281-6926-2

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

[Unknown]

Title of proceedings

IJCNN 2020 : Proceedings of the 2020 International Joint Conference on Neural Networks

Event

IEEE Computational Intelligence Society. Conference (2020 : Online from Glasgow, Scotland)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

IEEE Computational Intelligence Society Conference