# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 1 Introduction
Following that, the best model generated by the genetic algorithm (GA) is averaged. Brain tumor classification accuracy reached 94.2% on the tested dataset. Their method was implemented using a longer training time frame (100 epochs), and a holdout evaluation was conducted using only 615 images (500 + 115) for testing. Recently, [PERSON] et al. [22] proposed a decision support system utilizing pre-trained DenseNet201. Entropy-Kurtosis-based high feature values (EKbHFV) and modified GA (MGA) meta-heuristics were used for feature extraction. BRATS2018 and BRATS2019 datasets were evaluated using a multiclass SVM cubic classifier. The accuracy on BRATS2018 (BRATS2019) was 99.7% (99.8%) and 98.8% (99.3%) on glioblastoma (GBM/HGG) and lower grade glioma (LGG), respectively. The main limitation of their study is the removal of certain important features that affect the system's accuracy. A comparative study was proposed using five CNN architectures by [PERSON] et al. [23]. They modified the final layers of Xception, DenseNet201, DenseNet121, ResNet152V2, and InceptionResNetV2 with a deep dense block and softmax layer as the output layer. Using the Figshare dataset (3064 T1-w MRIs), they achieved an accuracy of 99.67% on the three-class dataset and 95.87% on the four-class (inclusive of healthy patients) dataset. The results show that the proposed model based on Xception architecture is the most suitable deep model for multi-class brain tumor classification. Despite the high accuracy, they applied fine-tuning parameters for the pre-trained models only using small-size training data. [PERSON] et al. [24] developed a DL model called MultiFeNet that uses multi-scale architecture for feature extraction. Instead of employing various kernel sizes, multi-scaling was implemented using a dilation rate. The introduced model was tested using five-fold cross-validation to achieve 96.4% for sensitivity, F1-score, precision, and accuracy. However, the multi-scale feature scaling increased system complexity, computational expense, and network training and hence optimization complexity. Also, there was a lack of cross-dataset generalization as the authors tested/trained their method on the same dataset. A transfer learning (TL)-based DL approach for the multi-class classification of brain tumor type via fine-tuning of pre-trained EfficientNets was proposed by [PERSON] et al. (2018). Five variants of modified EfficientNets were trained under different experimental settings. GradCAM-based visualization maps of modified EfficientNetB2 were applied to MR brain tumor sequences. The reported accuracy, precision, recall/sensitivity, and F1-score were 98.86%, 98.65%, 98.77%, and 98.71%, respectively. However, reduced accuracy of 91.53% was observed when performing cross-validation experiments on different datasets. A predictive CNN model using a hybrid generative adversarial network (GAN) was proposed by [PERSON] et al. (2018). Both GAN-augmented samples and the original augmented dataset were fed into an in-house CNN classification model. Among various GAN architectures, progressive-growing (PGGAN) demonstrated accuracy, precision, recall, F1-score, and NPV metrics of 98.8%, 98.45%, 97.2%, 98.11%, and 98.09%, respectively. Although the system showed promising results on various diseases, brain tumor recognition was evaluated in a small dataset, Figshare, using train/validation/test split only. [PERSON] et al. (2018) leveraged the benefits of using the 1 \(\times\) 1 convolution layer and TL to realize a fast classification brain tumor process. The method realized an average accuracy of 98.63%, using five TL iterations, and 98.86% using retrained k-fold cross-validation (internal TL between the folds, k = 5). Despite the fact that the method overcame the overfitting issue because of unnecessary parameters, it was trained only on the Figshare dataset with increased computational cost and struggled with noisy data. In (2018), a reinforcement learning-based architecture was introduced by [PERSON] et al.