# A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
## 1 Introduction
Their experiments showed accuracies of 87.54%, 89.72%, and 91.28% on estimated features using the spatial partition method. However, a smaller dataset and only engineered features were utilized for classification. Using principal component analysis (PCA), [PERSON] and [PERSON] [11] developed a brain tumor classification tool using linear discriminant analysis. They incorporated several hand-crafted features, such as intensity, texture, and shape, to label brain tissues as white matter, gray matter, CSF, abnormal, and normal. The classification was based on a support vector machine (SVM). However, their approach demonstrated a lower average accuracy of 0.83 (sensitivity: 0.88; specificity: 0.80); and their method employed hand-crafted features only and used traditional ML. [PERSON] et al. [12] proposed a structure for classifying brain tumors in MRIs based on gray wolf optimization and multiclass SVM. The gray wolf optimization technique performed better than the firefly algorithm and particle swarm optimization and reached a classification accuracy of 95.23%. Unlike the proposed system, they only employed GLCM for feature extraction and used a smaller dataset (3,064 images). [PERSON] and [PERSON] [13] integrated 2D discrete-wavelet transform and Gabor filtering to build a strong transform-domain statistical feature set to classify brain tumors. They used a back-propagation multilayer neural network using the derived statistical MRI characteristics. Also, their approach combined the two methods and achieved a fairly small score of 91.9% for accuracy using a small dataset. Similar to [10], [PERSON] et al. proposed a probabilistic neural network for brain tumor classification [14]. Inpreprocessing, they applied image filtering, sharpening, resizing, and contrast enhancement, as well as extracting texture features using GLCM. Their suggested method achieved an accuracy level of 83.33%. In addition to research work utilizing the extraction of hand-crafted features (e.g., [10; 11; 12; 13]), a model involving deep architecture to classify images in self-learning scenarios has also been developed. A modified capsule network called CapsNet was suggested by [PERSON] et al. [15]. This network used the spatial link between the brain lesion and the non-tumor tissues around it. The CapsNet architecture renders an overall accuracy of 88.33%. Although they used a modified neural architecture, the method scored a low accuracy score with a fairly longer training setting (50 epochs). [PERSON] et al. [16] used a CNN-based DL model to classify brain tumor images using five classification models. A ReLU layer and a maximum pool layer were included in the final architecture. The study claimed a validation accuracy of 84.19%. However, a shallow architecture was built and validated on a small dataset with three classes [17]. [PERSON] et al. [18] used the same data source as [16] and the same deep TL technique (GoogleNet) to classify the images. The images were analyzed for characteristics, which were then employed in compiling test and classification models. The authors reached an accuracy of 98% using a five-fold cross-validation. Although the overfitting was studied, system performance is affected by the training sample size reduction. Also, a high learning rate and a single classifier (SVM or KNN) were used. Similarly, [PERSON] et al. [19] exploited pre-trained CNNs and employed a block-wise fine-tuning mechanism using a pre-trained VGG19 and achieved an overall accuracy of 94.82%. A hybrid feature extraction method by [PERSON] et al. [20] was developed for classifying brain tumors. The feature vector was extracted using PCA and normalized GIST descriptors. Finally, a regularized extreme learning machine was proposed to classify brain tumors with 94.23% accuracy. Although their method showed improved classification accuracy, they used a hold-out evaluation method tested on 3064 images. A generic CNN-based algorithm consisting of six convolutional and max-pooling layers and only one fully connected layer was introduced by [PERSON] et al. [21].