An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine
Open accessAbstract
Super-resolution, which is one of the trend issues of recent times, increases the resolution of the images to higher levels. Increasing the resolution of a vital image in terms of the information it contains such as brain magnetic resonance image (MRI), makes the important information in the MRI image more visible and clearer. Thus, it is provided that the borders of the tumors in the related image are found more successfully. In this study, brain tumor detection based on fuzzy C-means with super-resolution and convolutional neural networks with extreme learning machine algorithms (SR-FCM-CNN) approach has been proposed. The aim of this study has been segmented the tumors in high performance by using Super Resolution Fuzzy-C-Means (SR-FCM) approach for tumor detection from brain MR images. Afterward, feature extraction and pretrained SqueezeNet architecture from convolutional neural network (CNN) architectures and classification process with extreme learning machine (ELM) were performed. In the experimental studies, it has been determined that brain tumors have been better segmented and removed using SR-FCM method. Using the SquezeeNet architecture, features were extracted from a smaller neural network model with fewer parameters. In the proposed method, 98.33% accuracy rate has been detected in the diagnosis of segmented brain tumors using SR-FCM. This rate is greater 10% than the rate of recognition of brain tumors segmented with fuzzy C-means (FCM) without SR.
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Özyurt et al. proposed a method that helps healthcare experts in the identification of benign and malignant tumors.
“Özyurt et al. [32] proposed a method that helps healthcare experts in the identification of benign and malignant tumors”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [32]
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- Authors
- Özyurt, F., Sert, E., & Avcı, D.
- Affiliations
- Fırat University (TR)
- Kahramanmaraş Sütçü İmam University (TR)
- Corresponding author
- Özyurt, Fatih
- Venue
- Medical Hypotheses
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 134
- Pages
- 109433
- ISSN
- 0306-9877 (ISSN-L)
- OpenAlex
- W2980671251
- Cited by
- 210
- References
- 33
- Citations by year
- 2026: 6 · 2025: 27 · 2024: 32 · 2023: 50 · 2022: 43 · 2021: 35 · 2020: 14 · 2019: 3
- FWCI
- 10.46
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- Medical Hypotheses (publishedVersion)
- PubMed (publishedVersion)
- DSpace - FIRAT (Fırat University) (submittedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- brain tumor detection, super-resolution, fuzzy c-means clustering, brain MRI segmentation, tumor segmentation, SqueezeNet, extreme learning machine, convolutional neural networks, feature extraction, expert systems
- MeSH terms
- Deep Learning, Algorithms, Brain Neoplasms, Diagnosis, Computer-Assisted, Expert Systems, Glioblastoma, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Software Design, Early Detection of Cancer, Neuroimaging
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