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Cited by 247Brain Tumor Detection and ClassificationAI in cancer detectionGlioma Diagnosis and Treatment

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm

Siar, Masoumeh, Teshnehlab, Mohammad·2019·2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)
DOI 10.1109/iccke48569.2019.8964846
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Abstract

Brain tumor can be classified into two types: benign and malignant. Timely and prompt disease detection and treatment plan leads to improved quality of life and increased life expectancy in these patients. One of the most practical and important methods is to use Deep Neural Network (DNN). In this paper, a Convolutional Neural Network (CNN) has been used to detect a tumor through brain Magnetic Resonance Imaging (MRI) images. Images were first applied to the CNN. The accuracy of Softmax Fully Connected layer used to classify images obtained 98.67%. Also, the accuracy of the CNN is obtained with the Radial Basis Function (RBF) classifier 97.34% and the Decision Tree (DT) classifier, is 94.24%. In addition to the accuracy criterion, we use the benchmarks of Sensitivity, Specificity and Precision evaluate network performance. According to the results obtained from the categorizers, the Softmax classifier has the best accuracy in the CNN according to the results obtained from network accuracy on the image testing. This is a new method based on the combination of feature extraction techniques with the CNN for tumor detection from brain images. The method proposed accuracy 99.12% on the test data. Due to the importance of the diagnosis given by the physician, the accuracy of the doctors help in diagnosing the tumor and treating the patient increased.

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Crossref · 10.1109/iccke48569.2019.8964846

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Research works printed but not linked: 2

  1. poonam, 2013, Review of image processing techniques for automatic detection of tumor in human brain, International Journal of Computer Science and Mobile Computing, 2, 117
  2. joshi, 2013, Survey of brain tumor detection techniques through MRI images, International Journal of Research in Engineering and Technology, 10, 586

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Metadata

Authors
Siar, M., & Teshnehlab, M.
Affiliations
  • Islamic Azad University, Tehran (IR)
  • K. N. Toosi University of Technology (IR)
ORCID
Venue
2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)
Publisher
IEEE
Published
2019-10
Pages
363-368
DOI
10.1109/iccke48569.2019.8964846
OpenAlex
W3001319889
Cited by
247
References
21
Citations by year
2026: 23 · 2025: 40 · 2024: 59 · 2023: 54 · 2022: 43 · 2021: 24 · 2020: 4
FWCI
10.40
Type
conference paper
Language
en
Open access
closed
Licence
Locations
International Conference on Computer and Knowledge Engineering (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Oncology › Glioma Diagnosis and Treatment
Keywords
brain tumor detection, brain MRI images, convolutional neural networks, benign and malignant tumor classification, softmax classifier, radial basis function classifier, decision tree classification, feature extraction, medical image classification

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