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
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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“The proposed method was tested on a single dataset, which lowers the credibility of the method.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [41]
Citations
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Cited by
Sorted by citations
Role of deep learning in brain tumor detection and classification (2015 to 2020): A review
Nazir, M., Shakil, S., & Khurshid, K. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101940 · Cited by 293
Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches
Younis, A., Li, Q., Nyatega, C. O., Adamu, M. J., & Kawuwa, H. B. (2022) · Applied Sciences · doi:10.3390/app12147282 · Cited by 288
Akter, A., Nosheen, N., Ahmed, S., Hossain, M., Yousuf, M. A., Almoyad, M. A. A., Hasan, K. F., & Moni, M. A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122347 · Cited by 272
Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images
Özkaraca, O., Bağrıaçık, O. İ., Gürüler, H., Khan, F. N., Hussain, J., Khan, J., & Laila, U. (2023) · Life · doi:10.3390/life13020349 · Cited by 222
MRI brain tumor detection using deep learning and machine learning approaches
Anantharajan, S., Gunasekaran, S., Thavasi, S., & Venkatesh, R. (2024) · Measurement Sensors · doi:10.1016/j.measen.2024.101026 · Cited by 204
Metadata
- Authors
- Siar, M., & Teshnehlab, M.
- Affiliations
- Islamic Azad University, Tehran (IR)
- K. N. Toosi University of Technology (IR)
- Venue
- 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 363-368
- 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
- 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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