A Novel Approach for Classifying Brain Tumours Combining a SqueezeNet Model with SVM and Fine-Tuning Article Swipe
Mohammed Rasool
,
Nor Azman Ismail
,
Arafat Al-Dhaqm
,
Wael M. S. Yafooz
,
Abdullah Alsaeedi
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12010149
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12010149
Cancer of the brain is most common in the elderly and young and can be fatal in both. Brain tumours can heal better if they are diagnosed and treated quickly. When it comes to processing medical images, the deep learning method is essential in aiding humans in diagnosing various diseases. Classifying brain tumours is an essential step that relies heavily on the doctor’s experience and training. A smart system for detecting and classifying these tumours is essential to aid in the non-invasive diagnosis of brain tumours using MRI (magnetic resonance imaging) images. This work presents a novel hybrid deep learning CNN-based structure to distinguish between three distinct types of human brain tumours through MRI scans. This paper proposes a method that employs a dual approach to classification using deep learning and CNN. The first approach combines the unsupervised classification of an SVM for pattern classification with a pre-trained CNN (i.e., SqueezeNet) for feature extraction. The second approach combines the supervised soft-max classifier with a finely tuned SqueezeNet. To evaluate the efficacy of the suggested method, MRI scans of the brain were used to analyse a total of 1937 images of glioma tumours, 926 images of meningioma tumours, 926 images of pituitary tumours, and 396 images of a normal brain. According to the experiment results, the finely tuned SqueezeNet model obtained an accuracy of 96.5%. However, when SqueezeNet was used as a feature extractor and an SVM classifier was applied, recognition accuracy increased to 98.7%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12010149
- https://www.mdpi.com/2079-9292/12/1/149/pdf?version=1672301997
- OA Status
- gold
- Cited By
- 43
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313328849
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313328849Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics12010149Digital Object Identifier
- Title
-
A Novel Approach for Classifying Brain Tumours Combining a SqueezeNet Model with SVM and Fine-TuningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-29Full publication date if available
- Authors
-
Mohammed Rasool, Nor Azman Ismail, Arafat Al-Dhaqm, Wael M. S. Yafooz, Abdullah AlsaeediList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12010149Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-9292/12/1/149/pdf?version=1672301997Direct link to full text PDF
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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://www.mdpi.com/2079-9292/12/1/149/pdf?version=1672301997Direct OA link when available
- Concepts
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Artificial intelligence, Support vector machine, Extractor, Computer science, Pattern recognition (psychology), Classifier (UML), Feature extraction, Deep learning, Magnetic resonance imaging, Computer vision, Radiology, Medicine, Engineering, Process engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
43Total citation count in OpenAlex
- Citations by year (recent)
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2025: 10, 2024: 15, 2023: 18Per-year citation counts (last 5 years)
- References (count)
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43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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