Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches

The detection of neurological disorders and diseases is aided by automatically identifying brain tumors from brain magnetic resonance imaging (MRI) images. A brain tumor is a potentially fatal disease that affects humans. Convolutional neural networks (CNNs) are the most common and widely used deep learning techniques for brain tumor analysis and classification. In this study, we proposed a deep CNN model for automatically detecting brain tumor cells in MRI brain images. First, we preprocess the 2D brain image MRI image to generate convolutional features. The CNN network is trained on the training dataset using the GoogleNet and AlexNet architecture, and the data model's performance is evaluated on the test data set. The model's performance is measured in terms of accuracy, sensitivity, specificity, and AUC. The algorithm performance matrices of both AlexNet and GoogLeNet are compared, the accuracy of AlexNet is 98.95, GoogLeNet is 99.45 sensitivity of AlexNet is 98.4, and GoogLeNet is 99.75, so from these values, we can infer that the GooGleNet is highly accurate and parameters that GoogLeNet consumes is significantly less; that is, the depth of AlexNet is 8, and it takes 60 million parameters, and the image input size is 227 × 227. Because of its high specificity and speed, the proposed CNN model can be a competent alternative support tool for radiologists in clinical diagnosis.

[1]  Fatima Rashid Sheykhahmad,et al.  Brain tumor diagnosis based on Zernike moments and support vector machine optimized by chaotic arithmetic optimization algorithm , 2023, Biomed. Signal Process. Control..

[2]  P. M. Ameer,et al.  Brain tumor categorization from imbalanced MRI dataset using weighted loss and deep feature fusion , 2023, Neurocomputing.

[3]  Huibin Wang,et al.  Contrastive Learning with Dynamic Weighting and Jigsaw Augmentation for Brain Tumor Classification in MRI , 2023, Neural Processing Letters.

[4]  Samah A. Gamel,et al.  Brain Tumor Detection and Classification Using Deep Learning and Sine-Cosine Fitness Grey Wolf Optimization , 2022, Bioengineering.

[5]  M. Irfan Uddin,et al.  A deep learning approach for brain tumor classification using MRI images , 2022, Computers and Electrical Engineering.

[6]  V. Dixit,et al.  Review of brain tumor detection from MRI images with hybrid approaches , 2022, Multimedia Tools and Applications.

[7]  V. Sasank,et al.  Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI images , 2022, Multimedia Tools and Applications.

[8]  T. Jemimma,et al.  Significant LOOP with clustering approach and optimization enabled deep learning classifier for the brain tumor segmentation and classification , 2021, Multim. Tools Appl..

[9]  Albert C. S. Chung,et al.  Relax and focus on brain tumor segmentation , 2021, Medical Image Anal..

[10]  Jagadeesh Kakarla,et al.  Three‐class brain tumor classification from magnetic resonance images using separable convolution based neural network , 2021, Concurr. Comput. Pract. Exp..

[11]  Sanjay Saxena,et al.  An empirical study of different machine learning techniques for brain tumor classification and subsequent segmentation using hybrid texture feature , 2021, Machine Vision and Applications.

[12]  Min Jiang,et al.  A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images. , 2021, Artificial intelligence in medicine.

[13]  Fatemeh Abdolkarimzadeh,et al.  Inverse dynamic finite element-optimization modeling of the brain tumor mass-effect using a variable pressure boundary , 2021, Comput. Methods Programs Biomed..

[14]  Nitin Kumar,et al.  Tumor bagging: a novel framework for brain tumor segmentation using metaheuristic optimization algorithms , 2021, Multimedia Tools and Applications.

[15]  W. A. Abro,et al.  A framework for efficient brain tumor classification using MRI images. , 2021, Mathematical biosciences and engineering : MBE.

[16]  Sa'ed Abed,et al.  Enhanced brain tumor classification using an optimized multi-layered convolutional neural network architecture , 2021, Multimedia Tools and Applications.

[17]  Naeem M. S. Hannon,et al.  Evaluation of brain tumor using brain MRI with modified-moth-flame algorithm and Kapur’s thresholding: a study , 2021, Evolutionary Intelligence.

[18]  Yuefeng Zhao,et al.  DFP-ResUNet: Convolutional Neural Network with a Dilated Convolutional Feature Pyramid for Multimodal Brain Tumor Segmentation , 2021, Comput. Methods Programs Biomed..

[19]  Y. L. Wang,et al.  CLCU-Net: Cross-level connected U-shaped network with selective feature aggregation attention module for brain tumor segmentation , 2021, Comput. Methods Programs Biomed..

[20]  Jagadeesh Kakarla,et al.  Three-class brain tumor classification using deep dense inception residual network , 2021, Soft Computing.

[21]  Hassan Khotanlou,et al.  Brain tumor classification using deep convolutional autoencoder-based neural network: multi-task approach , 2021, Multimedia Tools and Applications.

[22]  A. Srinivasa Reddy,et al.  MRI brain tumor segmentation and prediction using modified region growing and adaptive SVM , 2021, Soft Computing.

[23]  K. Karunakara,et al.  A comprehensive review on brain tumor segmentation and classification of MRI images , 2021, Multimedia Tools and Applications.

[24]  Jialin Peng,et al.  Brain tumor segmentation via C-dense convolutional neural network , 2021, Progress in Artificial Intelligence.

[25]  Mohamed Atri,et al.  Deep CNN for Brain Tumor Classification , 2021, Neural Processing Letters.

[26]  Jungong Han,et al.  Cross-modality deep feature learning for brain tumor segmentation , 2021, Pattern Recognit..

[27]  Srinivasalu Preethi,et al.  An efficient wavelet-based image fusion for brain tumor detection and segmentation over PET and MRI image , 2021, Multimedia Tools and Applications.

[28]  Rupal Agravat,et al.  A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction , 2021, Archives of Computational Methods in Engineering.

[29]  Mohammad R. Khosravi,et al.  Real-time classification of brain tumors in MRI images with a convolutional operator-based hidden Markov model , 2021, Journal of Real-Time Image Processing.

[30]  Jagadeesh Kakarla,et al.  Multi-class brain tumor classification using residual network and global average pooling , 2021, Multimedia Tools and Applications.

[31]  V. V. S. Sasank,et al.  Brain tumor classification using modified kernel based softplus extreme learning machine , 2021, Multimedia Tools and Applications.

[32]  Wan Mohd Nazmee Wan Zainon,et al.  Brain tumor classification in magnetic resonance image using hard swish-based RELU activation function-convolutional neural network , 2021, Neural Computing and Applications.

[33]  S. Sasikala,et al.  Segmentation and classification of brain tumors using modified median noise filter and deep learning approaches , 2021, Multimedia Tools and Applications.

[34]  Özlem Polat,et al.  Classification of brain tumors from MR images using deep transfer learning , 2021, The Journal of Supercomputing.

[35]  Ying Wang,et al.  Brain tumor segmentation in MR images using a sparse constrained level set algorithm , 2020, Expert Syst. Appl..

[36]  Lingling Zhang,et al.  A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors , 2020, Comput. Methods Programs Biomed..

[37]  Luping Zhou,et al.  Unsupervised brain tumor segmentation using a symmetric-driven adversarial network , 2021, Neurocomputing.

[38]  Milica M. Badža,et al.  Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network , 2020, Applied Sciences.