Cited by 433Brain Tumor Detection and ClassificationMedical Image Segmentation TechniquesAI in cancer detection

Brain tumor detection using fusion of hand crafted and deep learning features

Saba, Tanzila, Sameh Mohamed, Ahmed, El-Affendi, Mohammad, Amin, Javeria, Sharif, Muhammad·2020·Cognitive Systems Research

Abstract

Abstract The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor cells may lead to death. Therefore, early brain tumor diagnosis is a more momentous task that can save patient from adverse effects. In the proposed work, the Grab cut method is applied for accurate segmentation of actual lesion symptoms while Transfer learning model visual geometry group (VGG-19) is fine-tuned to acquire the features which are then concatenated with hand crafted (shape and texture) features through serial based method. These features are optimized through entropy for accurate and fast classification and fused vector is supplied to classifiers. The presented model is tested on top medical image computing and computer-assisted intervention (MICCAI) challenge databases including multimodal brain tumor segmentation (BRATS) 2015, 2016, and 2017 respectively. The testing results with dice similarity coefficient (DSC) achieve 0.99 on BRATS 2015, 1.00 on BRATS 2015 and 0.99 on BRATS 2017 respectively.

Integrity signals

Registry

Crossref · 10.1016/j.cogsys.2019.09.007

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Saba, T., Sameh Mohamed, A., El-Affendi, M., Amin, J., & Sharif, M.
Affiliations
  • Prince Sultan University (SA)
  • COMSATS University Islamabad (PK)
Corresponding author
Sharif, Muhammad
Venue
Cognitive Systems Research
Publisher
Elsevier BV
Published
2020-01
Volume
59
Pages
221-230
ISSN
1389-0417 (ISSN-L)
OpenAlex
W2974878728
Cited by
433
References
46
Citations by year
2026: 20 · 2025: 60 · 2024: 91 · 2023: 94 · 2022: 84 · 2021: 63 · 2020: 18 · 2019: 3
FWCI
18.54
Type
journal article
Language
en
Open access
closed
Locations
Cognitive Systems Research (publishedVersion)
Funders
Prince Sultan University
Topics
  • Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
brain tumor detection, brain tumor segmentation, feature fusion, handcrafted features, shape features, texture features, VGG19, transfer learning, GrabCut, entropy-based feature selection, BraTS, medical image classification

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review