A comparison of deep learning and hand crafted features in medical image modality classification

Modality corresponding to medical images is a vital filter in medical image retrieval systems, as radiologists or physicians are interested in only one of radiology images e.g CT scan, MRI, X-ray. Various handcrafted feature schemes have been proposed for medical image modality classification. On th...

Full description

Main Authors: Khan, S., Yong, S.-P.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.30523 /
Published: Institute of Electrical and Electronics Engineers Inc. 2016
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010289859&doi=10.1109%2fICCOINS.2016.7783289&partnerID=40&md5=66a0f39813dae6207de2741da4123fad
http://eprints.utp.edu.my/30523/
Tags: Add Tag
No Tags, Be the first to tag this record!
id utp-eprints.30523
recordtype eprints
spelling utp-eprints.305232022-03-25T07:09:56Z A comparison of deep learning and hand crafted features in medical image modality classification Khan, S. Yong, S.-P. Modality corresponding to medical images is a vital filter in medical image retrieval systems, as radiologists or physicians are interested in only one of radiology images e.g CT scan, MRI, X-ray. Various handcrafted feature schemes have been proposed for medical image modality classification. On the other hand not enough attempts have been made for deep learned feature extraction. A comparative evaluation of both handcrafted and deep learned features for medical image modality classification is presented in this paper. The experiments are performed on IMAGECLEF 2012 data. After carrying out the experiments it is shown that the handcrafted features outperforms the deep learned features and shows the potential of handcrafted feature extraction models in the medical image field. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010289859&doi=10.1109%2fICCOINS.2016.7783289&partnerID=40&md5=66a0f39813dae6207de2741da4123fad Khan, S. and Yong, S.-P. (2016) A comparison of deep learning and hand crafted features in medical image modality classification. In: UNSPECIFIED. http://eprints.utp.edu.my/30523/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Modality corresponding to medical images is a vital filter in medical image retrieval systems, as radiologists or physicians are interested in only one of radiology images e.g CT scan, MRI, X-ray. Various handcrafted feature schemes have been proposed for medical image modality classification. On the other hand not enough attempts have been made for deep learned feature extraction. A comparative evaluation of both handcrafted and deep learned features for medical image modality classification is presented in this paper. The experiments are performed on IMAGECLEF 2012 data. After carrying out the experiments it is shown that the handcrafted features outperforms the deep learned features and shows the potential of handcrafted feature extraction models in the medical image field. © 2016 IEEE.
format Conference or Workshop Item
author Khan, S.
Yong, S.-P.
spellingShingle Khan, S.
Yong, S.-P.
A comparison of deep learning and hand crafted features in medical image modality classification
author_sort Khan, S.
title A comparison of deep learning and hand crafted features in medical image modality classification
title_short A comparison of deep learning and hand crafted features in medical image modality classification
title_full A comparison of deep learning and hand crafted features in medical image modality classification
title_fullStr A comparison of deep learning and hand crafted features in medical image modality classification
title_full_unstemmed A comparison of deep learning and hand crafted features in medical image modality classification
title_sort comparison of deep learning and hand crafted features in medical image modality classification
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010289859&doi=10.1109%2fICCOINS.2016.7783289&partnerID=40&md5=66a0f39813dae6207de2741da4123fad
http://eprints.utp.edu.my/30523/
_version_ 1741197421246939136
score 11.62408

Similar Items