Three-class brain tumor classification using deep dense inception residual network

Abstract

Three-class brain tumor classification becomes a contemporary research task due to the distinct characteristics of tumors. The existing proposals employ deep neural networks for the three-class classification. However, achieving high accuracy is still an endless challenge in brain image classification. We have proposed a deep dense inception residual network for three-class brain tumor classification. We have customized the output layer of Inception ResNet v2 with a deep dense network and a softmax layer. The deep dense network has improved the classification accuracy of the proposed model. The proposed model has been evaluated using key performance metrics on a publicly available brain tumor image dataset having 3064 images. Our proposed model outperforms the existing model with a mean accuracy of 99.69%. Further, similar performance has been obtained on noisy data.

A Survey of Brain Tumor Segmentation and Classification Algorithms

“…The publicly available dataset provided by J. Cheng et al [138], which contains meningioma, glioma, and pituitary tumor in T1-WC MRI-images is one of the most commonly used datasets in the training and testing classifier models. Using this dataset, Gumaei, A. et al [125] has achieved a classification accuracy of 94.23% using a regularized extreme learning machine, while the Kokkalla, S. et al [153]…”

“…The publicly available dataset provided by J. Cheng et al [ 138 ], which contains meningioma, glioma, and pituitary tumor in T1-WC MRI-images is one of the most commonly used datasets in the training and testing classifier models. Using this dataset, Gumaei, A. et al [ 125 ] has achieved a classification accuracy of 94.23% using a regularized extreme learning machine, while the Kokkalla, S. et al [ 153 ] have reported a classification accuracy of 99.69% using custom modified deep-dense inception residual network (DDIRNet). These results indicate that the deep learning-based model outweighs the shallow machine learning-based techniques for this particular dataset.…”

Popular deep learning algorithms for disease prediction: a review

“…Previously, Rehman et al combined AlexNet, GoogleNet, and VGGNet with traditional machine learning models, and achieved good results, but if two different deep learning models can be combined, better results can be achieved. In 2021, Kokkalla et al [ 61 ] proposed a deep dense initial residual network model for the three-class classification of brain tumors, which customized the output layer of inception ResNet V2 with fully connected networks and softmax layer. In the same year, Ning et al [ 62 ] proposed an automatic Congestive Heart Failure (CHF) detection model based on a hybrid deep learning algorithm of CNN and Recursive Neural Network.…”

A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

“…This can be achieved by increasing the number of instances utilized for training purposes. Thus, it had evolved as a precise and very effective model [9,10]. Khan et al [11] projected an automated multi-modal classifier technique utilizing DL for classification of BT types.…”