Severity Level Classification of Brain Tumor based on MRI Images using Fractional-Chicken Swarm Optimization Algorithm

Brain tumor classification is highly effective in identifying and diagnosing the exact location of the tumor in the brain. The medical imaging system reported that early diagnosis and classification of the tumor increases the life of the human. Among various imaging modalities, magnetic resonance imaging (MRI) is highly used by clinical experts, as it offers contrast information of brain tumors. An effective classification method named fractional-chicken swarm optimization (fractional-CSO) is introduced to perform the severity-level tumor classification. Here, the chicken swarm behavior is merged with the derivative factor to enhance the accuracy of severity level classification. The optimal solution is obtained by updating the position of the rooster, which updates their location based on better fitness value. The brain images are pre-processed and the features are effectively extracted, and the cancer classification is carried out. Moreover, the severity level of tumor classification is performed using the deep recurrent neural network, which is trained by the proposed fractional-CSO algorithm. Moreover, the performance of the proposed fractional-CSO attained better performance in terms of the evaluation metrics, such as accuracy, specificity and sensitivity with the values of 93.35, 96 and 95% using simulated BRATS dataset, respectively.

[1]  Aboul Ella Hassanien,et al.  Brain MRI Tumor Segmentation with 3D Intracranial Structure Deformation Features , 2016, IEEE Intelligent Systems.

[2]  M. Varatharaj,et al.  MRI Brain Tumor Segmentation using Kernel Weighted Fuzzy Clustering , 2014 .

[3]  Mohammad Reza Daliri,et al.  Segmentation of brain MR images using a proper combination of DCS based method with MRF , 2018, Multimedia Tools and Applications.

[4]  Neeraj Sharma,et al.  An Optimized Cascaded Stochastic Resonance for the Enhancement of Brain MRI , 2018, IRBM.

[5]  Madhu S. Nair,et al.  Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier , 2018 .

[6]  I. Muchnik,et al.  Support Vector Machines for Classification , 2015 .

[7]  Poonam Yadav.  Case retrieval algorithm using similarity measure and fractional brain storm optimization for health informaticians , 2019, Int. Arab J. Inf. Technol..

[8]  Swapnil Gandhi,et al.  Auto-Encoders for Content-based Image Retrieval with its Implementation Using Handwritten Dataset , 2020, 2020 5th International Conference on Communication and Electronics Systems (ICCES).

[9]  Sanjeeva Polepaka,et al.  IDSS-based Two stage classification of brain tumor using SVM , 2019, Health and Technology.

[10]  S. Bauer,et al.  A survey of MRI-based medical image analysis for brain tumor studies , 2013, Physics in medicine and biology.

[11]  Peng Zhang,et al.  MRI brain tumor segmentation based on texture features and kernel sparse coding , 2019, Biomed. Signal Process. Control..

[12]  M. Suganthi,et al.  A Clinical Support System for Brain Tumor Classification Using Soft Computing Techniques , 2019, Journal of Medical Systems.

[13]  N. Varuna Shree,et al.  Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network , 2018, Brain Informatics.

[14]  Banshidhar Majhi,et al.  Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests , 2016, Neurocomputing.

[15]  P. Sathyanarayana,et al.  Image Texture Feature Extraction Using GLCM Approach , 2013 .

[16]  Devesh C. Jinwala,et al.  A Clustering Approach for the -Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm , 2014 .

[17]  C. Senthil Singh,et al.  BRAIN TUMOR MRI IMAGE SEGMENTATION AND DETECTION IN IMAGE PROCESSING , 2014 .

[18]  Seuc Ho Ryu,et al.  Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network , 2019, IEEE Access.

[19]  Mangesh B. Chaudhari,et al.  Compound gear-bearing fault feature extraction using statistical features based on time-frequency method , 2018, Measurement.

[20]  Y.-N. Li,et al.  Fast video shot boundary detection framework employing pre-processing techniques , 2009, IET Image Process..

[21]  Takeshi Nishida,et al.  Deep recurrent neural network for mobile human activity recognition with high throughput , 2017, Artificial Life and Robotics.

[22]  V. Anitha,et al.  Brain tumour classification using two-tier classifier with adaptive segmentation technique , 2016, IET Comput. Vis..

[23]  El-Sayed M. El-Horbaty,et al.  Classification using deep learning neural networks for brain tumors , 2017, Future Computing and Informatics Journal.

[24]  T Pandiselvi,et al.  Efficient Framework for Identifying, Locating, Detecting and Classifying MRI Brain Tumor in MRI Images , 2019, Journal of Medical Systems.

[25]  A. Ratna Raju,et al.  Bayesian HCS-based multi-SVNN: A classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering , 2018 .

[26]  M. Marsaline Beno,et al.  Threshold prediction for segmenting tumour from brain MRI scans , 2014, Int. J. Imaging Syst. Technol..

[27]  Sung Wook Baik,et al.  Multi-grade brain tumor classification using deep CNN with extensive data augmentation , 2019, J. Comput. Sci..

[28]  Inho Choi,et al.  Local Transform Features and Hybridization for Accurate Face and Human Detection , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Xiangyu Chang,et al.  Sparse Regularization in Fuzzy $c$ -Means for High-Dimensional Data Clustering , 2017, IEEE Transactions on Cybernetics.

[30]  Anuj Bhardwaj,et al.  A review on brain tumor segmentation of MRI images. , 2019, Magnetic resonance imaging.