A Densely Connected Convolutional Network Towards Efficient Recognition of Intracranial Hemorrhage Using CT Images

Authors

  • Md Harun Or Rashid Department of CSE, Bangladesh Army University of Engineering & Technology (BAUET), Qadirabad, Natore, Rajshahi, Bangladesh
  • Boshir Ahmed Department of CSE, Rajshahi University of Engineering & Technology (RUET), Rajshahi, Bangladesh

DOI:

https://doi.org/10.3329/gubjse.v9i1.74880

Keywords:

Intracranial Hemorrhage, CT images, DenseNet, Training, Testing, Recognition, RSNA

Abstract

Intracranial Bleeding also known as Intracranial Hemorrhage (ICH) is a severe issue for human beings and the most common cause of it is trauma. Majorly ICH occurs due to hypertension and around 2.5 per 10,000 people get affected by one of its subtypes. The human brain consists of lots of soft tissue and nerves, that’s why it is so tough to find affected areas within the shortest period and to apply proper treatment or medication for a radiologist by analyzing the Computed Tomography (CT) images. In addition to ICH treatment being expensive, a densely connected convolutional network (DenseNet-169) model is recommended to accurately detect the damaged region quickly and affordably to facilitate the treatment of the patient. All were private and inaccessible, with the exception of the CQ500 and the Radiological Society of North America (RSNA) datasets about ICH. In our study, we employed stage-2 of the RSNA dataset, which comprises  121,232 test images and 752,803 training images. Phase-1 and phase-2 are the two stages of the dataset. Among the  various preprocessing techniques, image type conversion, resizing, and normalization were performed on the dataset.  During the learning phase of our model, for hyper-tuning, a portion (30%) of training data was utilized as validation data.  The test data was then used to evaluate the model’s efficacy, and it was found that the ICH recognition accuracy of our  developed model was 98%. Index Terms— Intracranial Hemorrhage, CT images, DenseNet, Training, Testing, Recognition,  RSNA.

GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 13-22

Downloads

Download data is not yet available.
Abstract
59
PDF
39

Downloads

Published

2024-07-13

How to Cite

Rashid, M. H. O., & Ahmed, B. (2024). A Densely Connected Convolutional Network Towards Efficient Recognition of Intracranial Hemorrhage Using CT Images. GUB Journal of Science and Engineering, 9(1), 13–22. https://doi.org/10.3329/gubjse.v9i1.74880

Issue

Vol. 9 No. 1 (2022)

Section

Articles

License

Copyright (c) 2022 Green University of Bangladesh

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish in the GUB Journal of Science and Engineering agree to the following terms that:

  1. Authors retain copyright and grant the GUB Journal of Science and Engineering the right of first publication of the work.
  2. image
    Articles in GUB Journal of Science and Engineering are licensed under a Creative Commons CC BY-NC-ND License Attribution-NonCommercial-NoDerivatives 4.0 International License. This license permits Share — copy and redistribute the material in any medium or format.

Copyright and Reprint Permissions

  1. Individual contributions contained in it are protected by the copyright of Green University of Bangladesh.
  2. Photocopies of this journal in full or parts for personal or classroom usage may be allowed provided that copies are not made or distributed for profit or commercial advantage and the copies bear this notice and the full citation.
  3. Copyright for components of this work owned by others must be honored. Abstracting with credit is permitted.
  4. Specific permission of the publisher and payment of a fee are required for multiple or systemic copying, advertising or promotional purposes, resale, republishing, posting on servers, redistributing to lists and all forms of document delivery.
  5. Subscribers may reproduce a table of contents or prepare lists of articles including abstracts for internal circulation within their institutions.
  6. Permission of the Publisher is required for resale and distribution outside the institution. Permission of the publisher is required for all other derivative works, including compilations and translations.
  7. Except as outlined above, no part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, such as electronic, mechanical, photocopying, magnetic recording or otherwise, without prior written permission of the publisher.
  8. Permissions may be sought directly from the office of the executive editor of GUB Journal of Science and Engineering through E-mail at gubjse@fse.green.edu.bd.

Notice

  • Responsibility for the contents of an article rests upon the author(s) and not upon the editor or the publisher. Therefore, on responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, product instructions or ideas contained in the material herein.