## 2 Related Works  
The authors in [13] developed a BT classification method based on the hybrid brain tumor classification (HBTC) model. The presented model reduces the intrinsic difficulties and enhances the classification performance. Additionally, many ML models such as multilayer perception (MLP), J48, meta bagging (MB), and random tree (RT) are used for the classification of cyst, glioma, menin, and meta tumors. The authors in [14] presenteda multi-level attention model to classify BT. The presented multi-level attention network (MANet) comprises spatial and cross-channel attention that concentrates on tumor region prioritization and manages cross-channel temporal dependency existing in the semantic feature sequence attained from the Xception backbone. [PERSON] et al. [15] presented a CNN-based dense EfficientNet using min-max normalization for recognizing 3260 T1-weighted contrast-enhanced brain MRI images into four categories (glioma, meningioma, pituitary, and no tumor). The developed network is a version of EfficientNet with dense and drop-out layers appended. In addition, data augmentation with min-max normalization is integrated to increase the contrast of tumor cells.  
[PERSON] et al. [16] developed a differential deep convolutional neural network (differential deep-CNN) architecture to categorize a variety of BTs, involving normal and abnormal MR. A further differential feature map from the original CNN feature map can be derived by utilizing a differential operator in the deep-CNN model. The derivation procedure results in increasing the performance of the presented technique based on the result of the evaluation parameter. [PERSON] et al. [17] introduced a custom Mask Region-based CNN (Mask RCNN) with a densenet-41 backbone structural design viz., trained through transfer learning (TL) for accurate segmentation and classifying of brain cancers.  
In [18], the authors proposed a Fully Automated Heterogeneous Segmentation using SVM (FAHS-SVM) for brain cancer segmentation dependent upon DL technologies. The study presents the cerebral venous system separation into MRI images by adding a novel, fully automated technique dependent upon morphological, relaxometry, and structural details. The segmentation function can discriminate with a higher level of uniformity among anatomy and the neighboring brain tissues. ELM is a kind of learning model that comprises more than one hidden node layer. [PERSON] et al. [19] combined a deep neural network with the discrete wavelet transform (DWT). The useful feature extracting mechanism and principal components analysis (PCA) and performance assessment were very effective on the performance measure.  
[PERSON] et al. [20] explored the efficiency of a new method to classify brain cancer MRI using VGG19 feature extracting combined with one of three kinds of the classifier. A progressive, growing generative adversarial network (PGGAN) augmentation module was utilized for producing a'realistic' MRI of BT and assisted in resolving the shortcomings of the image required for DL. In [PERSON] et al. [21], a 2-channel DNN structure is presented for tumor classifier viz. further generalizable. At first, local feature representation is extracted from the convolutional block of Xception and InceptionResNetV2 networks and is vectorized by the presented pooling-based models. An attention model is presented, which focuses more on the tumor region and less on the non-tumor region, ultimately assisting in distinguishing the kind of tumor from the images. [PERSON] et al. [16] introduce CNNs (VGGNet, AlexNet, and GoogLeNet) for categorizing BTs, namely pituitary, glioma, and meningioma. Then, the study examines the transfer learning technique that freezes and fine-tunes using MRI slices of BT data.