A Robust Hybrid Convolutional Network for Tumor Classification Using Brain MRI Image Datasets

Brain tumour detection is challenging for experts or doctors in the early stage. Many advanced techniques are used for the detection of different cancers and analysis using different medical images. Deep learning (DL) comes under artificial intelligence, which is used to analyse and characterisation medical image processing and also finds the classification of brain cancer. Magnetic Resonance Imaging (MRI) has become the keystone in brain cancer recognition and the fusion of advanced imaging methods with cutting-edge DL models has exposed great potential in enhancing accuracy. This research aims to develop an efficient hybrid CNN model by employing support vector machine (SVM) classifiers to advance the efficacy and stability of the projected convolutional neural network (CNN) model. Two distinct brain MRI image datasets (Dataset_MC and Dataset_BC) are binary and multi-classified using the suggested CNN and hybrid CNN-SVM (Support Vector Machine) models. The suggested CNN model employs fewer layers and parameters for feature extraction, while SVM functions as a classifier to preserve maximum accuracy in a shorter amount of time. The experiment result shows the evaluation of the projected CNN model with the SVM for the performance evaluation, in which CNN-SVM give the maximum accuracy on the test datasets at 99% (Dataset_BC) and 98% (Dataset_MC) as compared to other CNN models.

@article{Bansal2024,
  title     = {A Robust Hybrid Convolutional Network for Tumor Classification Using Brain MRI Image Datasets},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Satish Bansal and Rakesh S Jadon and Sanjay K. Gupta},
  doi       = {10.14569/IJACSA.2024.0150459},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150459}
}