Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification
One of the top causes of mortality in people globally is a brain tumor. Today, biopsy is regarded as the cornerstone of cancer diagnosis. However, it faces difficulties, including low sensitivity, hazards during biopsy treatment, and a protracted waiting period for findings. In this context, develop...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/4/668 |
| _version_ | 1797621530254901248 |
|---|---|
| author | Suganya Athisayamani Robert Singh Antonyswamy Velliangiri Sarveshwaran Meshari Almeshari Yasser Alzamil Vinayakumar Ravi |
| author_facet | Suganya Athisayamani Robert Singh Antonyswamy Velliangiri Sarveshwaran Meshari Almeshari Yasser Alzamil Vinayakumar Ravi |
| author_sort | Suganya Athisayamani |
| collection | DOAJ |
| description | One of the top causes of mortality in people globally is a brain tumor. Today, biopsy is regarded as the cornerstone of cancer diagnosis. However, it faces difficulties, including low sensitivity, hazards during biopsy treatment, and a protracted waiting period for findings. In this context, developing non-invasive and computational methods for identifying and treating brain cancers is crucial. The classification of tumors obtained from an MRI is crucial for making a variety of medical diagnoses. However, MRI analysis typically requires much time. The primary challenge is that the tissues of the brain are comparable. Numerous scientists have created new techniques for identifying and categorizing cancers. However, due to their limitations, the majority of them eventually fail. In that context, this work presents a novel way of classifying multiple types of brain tumors. This work also introduces a segmentation algorithm known as Canny Mayfly. Enhanced chimpanzee optimization algorithm (EChOA) is used to select the features by minimizing the dimension of the retrieved features. ResNet-152 and the softmax classifier are then used to perform the feature classification process. Python is used to carry out the proposed method on the Figshare dataset. The accuracy, specificity, and sensitivity of the proposed cancer classification system are just a few of the characteristics that are used to evaluate its overall performance. According to the final evaluation results, our proposed strategy outperformed, with an accuracy of 98.85%. |
| first_indexed | 2024-03-11T08:57:19Z |
| format | Article |
| id | doaj.art-c5e537762f11496ebcfac2535077c399 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T08:57:19Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-c5e537762f11496ebcfac2535077c3992023-11-16T20:01:15ZengMDPI AGDiagnostics2075-44182023-02-0113466810.3390/diagnostics13040668Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor ClassificationSuganya Athisayamani0Robert Singh Antonyswamy1Velliangiri Sarveshwaran2Meshari Almeshari3Yasser Alzamil4Vinayakumar Ravi5School of Computing, Sastra Deemed to be University, Thanjavur 613401, IndiaDepartment of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, IndiaDepartment of Computational Intelligence, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, IndiaDepartment of Diagnostic Radiology, College of Applied Medical Sciences, University of Ha’il, Ha’il 55476, Saudi ArabiaDepartment of Diagnostic Radiology, College of Applied Medical Sciences, University of Ha’il, Ha’il 55476, Saudi ArabiaCenter for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi ArabiaOne of the top causes of mortality in people globally is a brain tumor. Today, biopsy is regarded as the cornerstone of cancer diagnosis. However, it faces difficulties, including low sensitivity, hazards during biopsy treatment, and a protracted waiting period for findings. In this context, developing non-invasive and computational methods for identifying and treating brain cancers is crucial. The classification of tumors obtained from an MRI is crucial for making a variety of medical diagnoses. However, MRI analysis typically requires much time. The primary challenge is that the tissues of the brain are comparable. Numerous scientists have created new techniques for identifying and categorizing cancers. However, due to their limitations, the majority of them eventually fail. In that context, this work presents a novel way of classifying multiple types of brain tumors. This work also introduces a segmentation algorithm known as Canny Mayfly. Enhanced chimpanzee optimization algorithm (EChOA) is used to select the features by minimizing the dimension of the retrieved features. ResNet-152 and the softmax classifier are then used to perform the feature classification process. Python is used to carry out the proposed method on the Figshare dataset. The accuracy, specificity, and sensitivity of the proposed cancer classification system are just a few of the characteristics that are used to evaluate its overall performance. According to the final evaluation results, our proposed strategy outperformed, with an accuracy of 98.85%.https://www.mdpi.com/2075-4418/13/4/668spatial gray level dependence matrixCanny algorithmmodified chimp optimization algorithmsoftmax classifierdeep convolutional neural network |
| spellingShingle | Suganya Athisayamani Robert Singh Antonyswamy Velliangiri Sarveshwaran Meshari Almeshari Yasser Alzamil Vinayakumar Ravi Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification Diagnostics spatial gray level dependence matrix Canny algorithm modified chimp optimization algorithm softmax classifier deep convolutional neural network |
| title | Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification |
| title_full | Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification |
| title_fullStr | Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification |
| title_full_unstemmed | Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification |
| title_short | Feature Extraction Using a Residual Deep Convolutional Neural Network (ResNet-152) and Optimized Feature Dimension Reduction for MRI Brain Tumor Classification |
| title_sort | feature extraction using a residual deep convolutional neural network resnet 152 and optimized feature dimension reduction for mri brain tumor classification |
| topic | spatial gray level dependence matrix Canny algorithm modified chimp optimization algorithm softmax classifier deep convolutional neural network |
| url | https://www.mdpi.com/2075-4418/13/4/668 |
| work_keys_str_mv | AT suganyaathisayamani featureextractionusingaresidualdeepconvolutionalneuralnetworkresnet152andoptimizedfeaturedimensionreductionformribraintumorclassification AT robertsinghantonyswamy featureextractionusingaresidualdeepconvolutionalneuralnetworkresnet152andoptimizedfeaturedimensionreductionformribraintumorclassification AT velliangirisarveshwaran featureextractionusingaresidualdeepconvolutionalneuralnetworkresnet152andoptimizedfeaturedimensionreductionformribraintumorclassification AT mesharialmeshari featureextractionusingaresidualdeepconvolutionalneuralnetworkresnet152andoptimizedfeaturedimensionreductionformribraintumorclassification AT yasseralzamil featureextractionusingaresidualdeepconvolutionalneuralnetworkresnet152andoptimizedfeaturedimensionreductionformribraintumorclassification AT vinayakumarravi featureextractionusingaresidualdeepconvolutionalneuralnetworkresnet152andoptimizedfeaturedimensionreductionformribraintumorclassification |