The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture
Open accessAbstract
Brain tumor is one of the dangerous and deadly cancer types seen in adults and children. Early and accurate diagnosis of brain tumor is important for the treatment process. It is an important step for specialists to detect the brain tumor using computer aided systems. These systems allow specialists to perform tumor detection more easily. However, mistakes made with traditional methods are also prevented. In this paper, it is aimed to diagnose the brain tumor using MRI images. CNN models, one of the deep learning networks, are used for the diagnosis process. Resnet50 architecture, one of the CNN models, is used as the base. The last 5 layers of the Resnet50 model have been removed and added 8 new layers. With this model, 97.2% accuracy value is obtained. Also, results are obtained with Alexnet, Resnet50, Densenet201, InceptionV3 and Googlenet models. Of all these models, the model developed with the highest performance has classified the brain tumor images. As a result, when analyzed in other studies in the literature, it is concluded that the developed method is effective and can be used in computer-aided systems to detect brain tumor.
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Cites
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Cited by
Multiclass Brain Tumor Classification Using Convolutional Neural Network and Support Vector Machine
Kibriya, H., Masood, M., Nawaz, M., Rafique, R., & Rehman, S. (2021) · 2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) · doi:10.1109/majicc53071.2021.9526262 · Cited by 50
Metadata
- Authors
- Çinar, A., & Yildirim, M.
- Venue
- Medical Hypotheses
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 139
- Pages
- 109684
- ISSN
- 0306-9877 (ISSN-L)
- OpenAlex
- W3014047622
- Cited by
- 432
- References
- 22
- FWCI
- 20.32
- Type
- journal article
- Language
- en
- Open access
- green
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