Political exponential deer hunting optimization-based deep learning for brain tumor classification using MRI

In this work, three deep learning classifiers, namely pyramid scene parsing network (PSPNet), Shepard convolutional neural network (ShCNN) and deep convolutional neural network (Deep CNN), are introduced to segment, detect and classify the tumor region from magnetic resonance images (MRI). The pre-p...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2023-10, Vol.17 (7), p.3451-3459
Main Authors: Rajakumar, S., Agalya, V., Rajeswari, R., Pachlor, Rohit
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Classification
Classifiers
Computer Imaging
Computer Science
Deep learning
Deer
Edema
Hunting
Image Processing and Computer Vision
Machine learning
Magnetic resonance imaging
Medical imaging
Multimedia Information Systems
Neural networks
Optimization
Original Paper
Pattern Recognition and Graphics
Segments
Sensitivity
Signal,Image and Speech Processing
Tumors
Vision
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Summary:In this work, three deep learning classifiers, namely pyramid scene parsing network (PSPNet), Shepard convolutional neural network (ShCNN) and deep convolutional neural network (Deep CNN), are introduced to segment, detect and classify the tumor region from magnetic resonance images (MRI). The pre-processing is carried out by exploiting region of interest extraction that removes the irrelevant noise that exists in the images. The PSPNet segments the tumor region from the MRI to progress the efficacy of classification. The ShCNN detects the segmented image as either normal or abnormal image. Moreover, the tumor classification is completed using Deep CNN, which classifies the detected image as either benign, edema, core or malignant. In addition, the training process of three classifiers is performed by exploiting the adopted political exponential deer hunting optimization algorithm, which is designed by incorporating political optimizer, and exponential deer hunting optimization. Moreover, the performance analysis proved that the adopted tumor detection method obtained the optimal performance based on the testing accuracy, sensitivity, as well as specificity of 0.904, 0.919 and 0.899, and the developed classification approach obtained maximum testing accuracy, sensitivity, as well as specificity of 0.929, 0.935 and 0.902, respectively.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-023-02567-2