# A CNN-Based Strategy to Classify MRI-Based Brain Tumors Using Deep Convolutional Network
## 2 Related Works  
Various approaches have been developed in recent years to recognize brain tumors on MRI images. SVM and Neural Networks (NN) are the most widely used techniques due to their excellent performance over the past decade [31].  
A Probabilistic Neural Network (PNN) is used where the decision-making strategy partitioned into extraction utilizing vital component investigation and classification using PNN, with an accuracy ranging from 100% to 73% depending on the spread value [32].  
A CNN-based deep learning model is proposed for categorizing various forms of brain tumors, where the architecture has a classification accuracy of 96.13% for the categorization of brain tumor types [33].  
Extreme Learning Machine of Local Receptive Fields (ELM-LRF) is a new deep learning paradigm that covers two distinct structures in its body [31]. An ELM-LRF model is created with four adjustable parameters, the convolution filter size r, the number of convolution filters K, the pooling size, and the regulatory coefficient C. The proposed ELM-LRF approach yielded a classification accuracy of 97.18% [34].  
A Deep Neural Network (DNN) is used as a discriminator in a Generative Adversarial Network (GAN) to extract powerful features and grasp the structure of MRI images in its convolutional layers, resulting in 95.6% accuracy [35].  
Multiple Kernel-Based Probabilistic Clustering (MKPC) is used to segment the image, and a deep learning classifier is used to categorize it, achieving an accuracy of 0.83% [36].  
A CNN technique is proposed, where the Fuzzy C-Means (FCM) method is used for brain segmentation and texture and form properties from these segmented areas were recovered before being sent into the SVM and DNN processors, with an accuracy of 97.4% [37].  
A tiny kernel CNN model can also be used for the classification with 3 \(\times\) 3 kernels for all convolutional layers with 1 stride. This result continues to demonstrate a 90.67% precision in the augmented dataset [38].  
In a study, a DNN classifier combined with the Discrete Wavelet Transform (DWT) achieved a good result of 96.77% in a tiny dataset [39].  
A deep learning technique is provided to classify multimodal brain tumors using a linear contrast augmentation approach. Before fusion, features are extracted using transfer learning from two distinct CNN models. ELM is used to classify the robust properties obtained by this technique [40].  
Several researchers employed pre-trained CNN architectures and fine-tuned them for brain tumor classification. The proposed categorization system extracts attributes from brain MRI images using a pre-trained GoogLeNet. After that, by using proven classifier models and a five-fold cross-validation technique, the experiment obtains 97% accuracy [41].  
The Grab cut method is used to properly segment the real lesion [17]. This study shows segmentation utilizing Unet architecture with ResNet50 as a baseline. The application of evolutionary methods (ResNet50, DenseNet201, MobileNet V2, and InceptionV3) and reinforcement learning through transfer learning achieves an accuracy of 91.8%, 92.8%, 92.9%, 93.1%, and 99.6%, respectively, in the categorization of brain cancers.  
A multi-level CNN model is introduced, where pre-trained models, such as ResNet-50, VGG-16, and Inception V3, are used to generate trained parameters, achieving 99.89% classification accuracy [42].  
A hybrid deep learning model called DeepTumorNet is proposed, which is generated by modifying the layers of GoogleNet architecture, with the addition of leaky ReLU activation function. This architecture achieved 99.67% accuracy [43].  
A differential deep convolutional neural network model is suggested, where the differential feature maps of CNN are derived using differential operators, which resulted in an accuracy of 99.25% [44].  
Some of the other state-of-the-art work are shown in Table 1.