Title:Automated Brain Tumor Detection using Ideal Shallow Neural Network with
Artificial Jellyfish Optimization
Volume: 20
Author(s): Salem Rajagopalan Sridhar*, Muthuramalingam Akila and Ramasamy Asokan
Affiliation:
- Department of Computer Science and Engineering, Muthayammal Engineering College, Namakkal, 637408, India
Keywords:
Brain tumor, Magnetic resonance imaging, Gabor filtering, Multi-set feature extraction, Artificial jellyfish optimization, Ideal shallow neural network, Grasshopper optimization algorithm methodology, Centroid weighted segmentation.
Abstract:
Introduction:
Brain tumors are predicted from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan images. In recent years, image
processing-based automated tools are developed to predict tumor areas with less human interference. However, such automated tools are suffering
from computational complexity and reduced accuracy in certain critical images. In the proposed work, an Ideal Shallow Neural Network (ISNN) is
utilized to improve the prediction accuracy, and the computational complexity is reduced by implementing an Artificial Jellyfish Optimization
(AJO) algorithm for minimizing the feature dimensionality.
Methods:
The proposed method utilizes MRI images for the verification process as they are more informative than the CT scan image. The BRATS and the
Kaggle datasets are used in this work and a Gabor filtering technique is used for noise reduction and a histogram equalization is used for enhancing
the tumor boundary regions. The classification results observed from the AJO-ISNN are further forwarded towards the segmentation process and
which uses the Centroid Weighted Segmentation (WCS) along with a Grasshopper Optimization Algorithm (GOA) for improving the segmentation
over the boundary regions of the brain tumor.
Results:
The experimental result indicates a classification accuracy of 95.14% on the proposed AJO-ISNN model and AJO-ISNN is comparatively better
than the Convolutional Neural Network (CNN) model accuracy of 85.41% and VGG 19 model accuracy of 93.75% while implemented with the
AJO optimization model. Similarly, the Dice Similarity Coefficient of the proposed CWS-GOA also reaches 93.15% when performed with both
BRATS and Kaggle datasets.
Conclusion:
Apart from the accuracy attainments the proposed work classifies and segments the tumor region in around 65 seconds on average of 200 image
verifications and that is comparatively better than the previous multi-cascaded CNN and the InceptionV3 models.
