Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches Article Swipe
Chetan Swarup
,
Kamred Udham Singh
,
Ankit Kumar
,
Saroj Kumar Pandey
,
Neeraj Varshney
,
Teekam Singh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3934/era.2023146
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3934/era.2023146
The detection of neurological disorders and diseases is aided by automatically identifying brain tumors from brain magnetic resonance imaging (MRI) images. A brain tumor is a potentially fatal disease that affects humans. Convolutional neural networks (CNNs) are the most common and widely used deep learning techniques for brain tumor analysis and classification. In this study, we proposed a deep CNN model for automatically detecting brain tumor cells in MRI brain images. First, we preprocess the 2D brain image MRI image to generate convolutional features. The CNN network is trained on the training dataset using the GoogleNet and AlexNet architecture, and the data model's performance is evaluated on the test data set. The model's performance is measured in terms of accuracy, sensitivity, specificity, and AUC. The algorithm performance matrices of both AlexNet and GoogLeNet are compared, the accuracy of AlexNet is 98.95, GoogLeNet is 99.45 sensitivity of AlexNet is 98.4, and GoogLeNet is 99.75, so from these values, we can infer that the GooGleNet is highly accurate and parameters that GoogLeNet consumes is significantly less; that is, the depth of AlexNet is 8, and it takes 60 million parameters, and the image input size is 227 × 227. Because of its high specificity and speed, the proposed CNN model can be a competent alternative support tool for radiologists in clinical diagnosis.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3934/era.2023146
- OA Status
- gold
- Cited By
- 23
- References
- 41
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4327653197Canonical identifier for this work in OpenAlex
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https://doi.org/10.3934/era.2023146Digital Object Identifier
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Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approachesWork title
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articleOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-01-01Full publication date if available
- Authors
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Chetan Swarup, Kamred Udham Singh, Ankit Kumar, Saroj Kumar Pandey, Neeraj Varshney, Teekam SinghList of authors in order
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https://doi.org/10.3934/era.2023146Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3934/era.2023146Direct OA link when available
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Convolutional neural network, Artificial intelligence, Computer science, Pattern recognition (psychology), Deep learning, Data set, Test set, Set (abstract data type), Programming languageTop concepts (fields/topics) attached by OpenAlex
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23Total citation count in OpenAlex
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2025: 7, 2024: 15, 2023: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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