The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Classification using deep learning neural networks for brain tumors
Open accessAbstract
Deep Learning is a new machine learning field that gained a lot of interest over the past few years. It was widely applied to several applications and proven to be a powerful machine learning tool for many of the complex problems. In this paper we used Deep Neural Network classifier which is one of the DL architectures for classifying a dataset of 66 brain MRIs into 4 classes e.g. normal, glioblastoma, sarcoma and metastatic bronchogenic carcinoma tumors. The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool and principal components analysis (PCA) and the evaluation of the performance was quite good over all the performance measures.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
State of the art survey on MRI brain tumor segmentation
Gordillo, N., Montseny, E., & Sobrevilla, P. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2013.05.002 · Cited by 697
Cited by
Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture
Çinar, A., & Yildirim, M. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.109684 · Cited by 432
Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification
Mzoughi, H., Njeh, I., Wali, A., Slima, M. B., BenHamida, A., Mhiri, C., & Mahfoudhe, K. B. (2020) · Journal of Digital Imaging · doi:10.1007/s10278-020-00347-9 · Cited by 361
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.05.028 · Cited by 348
Convolutional neural networks for multi-class brain disease detection using MRI images
Talo, M., Yildirim, O., Baloglu, U. B., Aydin, G., & Acharya, U. R. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101673 · Cited by 301
Brain Tumor Detection and Classification Using Intelligence Techniques: An Overview
Solanki, S., Singh, U. P., Chouhan, S. S., & Jain, S. (2023) · IEEE Access · doi:10.1109/access.2023.3242666 · Cited by 259
Metadata
- Authors
- Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M., & Salem, A.-B. M.
- Venue
- Future Computing and Informatics Journal
- Publisher
- Future University in Egypt
- Published
- 2018-06
- Volume
- 3
- Issue
- 1
- Pages
- 68-71
- ISSN
- 2314-7288 (ISSN-L)
- OpenAlex
- W2780099243
- Cited by
- 1,074
- References
- 2
- FWCI
- 23.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Classification using deep learning neural networks for brain tumors. https://app.cochat.ai/papers/10.1016/j.fcij.2017.12.001
- The paper
- Mohsen, H., El-Dahshan, E.-S. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal. https://doi.org/10.1016/j.fcij.2017.12.001
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
