An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine.

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.

[1]  Dwarikanath Mahapatra,et al.  Image super-resolution using progressive generative adversarial networks for medical image analysis , 2019, Comput. Medical Imaging Graph..

[2]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[3]  Fatih Özyurt,et al.  A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine , 2020, Soft Comput..

[4]  Eser Sert,et al.  Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy , 2019 .

[5]  Engin Avci,et al.  A Novel Liver Image Classification Method Using Perceptual Hash-Based Convolutional Neural Network , 2018, Arabian Journal for Science and Engineering.

[6]  Norberto Malpica,et al.  Single-image super-resolution of brain MR images using overcomplete dictionaries , 2013, Medical Image Anal..

[7]  Xiaoqiang Lu,et al.  MR image super-resolution via manifold regularized sparse learning , 2015, Neurocomputing.

[8]  Takeo Kanade,et al.  Limits on Super-Resolution and How to Break Them , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Geethu Mohan,et al.  MRI based medical image analysis: Survey on brain tumor grade classification , 2018, Biomed. Signal Process. Control..

[10]  Syed Abdul Rahman Syed Abu Bakar,et al.  Review of Brain Lesion Detection and Classification using Neuroimaging Analysis Techniques , 2015 .

[11]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[12]  Prabin Kumar Bora,et al.  A novel diagnostic information based framework for super-resolution of retinal fundus images , 2019, Comput. Medical Imaging Graph..

[13]  Yu Yang,et al.  Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network , 2018, Comput. Biol. Medicine.

[14]  Eduardo Romero,et al.  A sparse Bayesian representation for super-resolution of cardiac MR images. , 2017, Magnetic resonance imaging.

[15]  Weimin Huang,et al.  Brain tumor grading based on Neural Networks and Convolutional Neural Networks , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Wangmeng Zuo,et al.  Learning a Single Convolutional Super-Resolution Network for Multiple Degradations , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[17]  Yun Fu,et al.  Image Super-Resolution Using Very Deep Residual Channel Attention Networks , 2018, ECCV.

[18]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[19]  Vinod Kumar,et al.  A package-SFERCB-"Segmentation, feature extraction, reduction and classification analysis by both SVM and ANN for brain tumors" , 2016, Appl. Soft Comput..

[20]  Sundaram Suresh,et al.  Performance enhancement of extreme learning machine for multi-category sparse data classification problems , 2010, Eng. Appl. Artif. Intell..

[21]  Qinghua Zhao,et al.  Brain tumor classification for MR images using transfer learning and fine-tuning , 2019, Comput. Medical Imaging Graph..

[22]  Georgios Tzimiropoulos,et al.  Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses with GANs , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[23]  Qionghua Wang,et al.  Medical image super-resolution by using multi-dictionary and random forest , 2018 .

[24]  P. M. Ameer,et al.  Brain tumor classification using deep CNN features via transfer learning , 2019, Comput. Biol. Medicine.

[25]  Akif Dogantekin,et al.  A novel approach for liver image classification: PH-C-ELM , 2019, Measurement.

[26]  Sheng Ren,et al.  Towards efficient medical lesion image super-resolution based on deep residual networks , 2019, Signal Process. Image Commun..

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

[28]  K. Manivannan,et al.  Fusion based Glioma brain tumor detection and segmentation using ANFIS classification , 2018, Comput. Methods Programs Biomed..

[29]  Dinggang Shen,et al.  Super-resolution reconstruction of neonatal brain magnetic resonance images via residual structured sparse representation , 2019, Medical Image Anal..