## 1 Introduction
In recent years, numerous automated systems have been used to detect brain tumors using MRI scans. [PERSON] et al. [7] classified brain tumors into various types using various methods, namely:region-of-interest (ROI) determination; feature extraction; and feature selection followed by classification. They combined local texture along with global histogram moments and estimated the effects of gliomas quantitatively using 107 images, 73 low- and 34 high-grade images (glioma). [PERSON] et al. [8] illustrated a Computer-Aided Diagnosis (CAD) system, extracting color and textural features of ROIs that were segmented, and used a Genetic Algorithm (GA) for selecting optimal features. They achieved an accuracy of 91.70% and 94.90% using Genetic Algorithm-based Support Vector Machine (GA-SVM) and GA-based Artificial Neural Network (GA-ANN), respectively. [PERSON] et al. [9] used a publicly available T1-weighted Contrast-Enhanced Magnetic Resonance Images (CE-MRI) dataset [10], consisting of brain MRI scans having: glioma-; meningioma-; and pituitary-tumors, and applied three features extraction methods: intensity histogram; bag-of-words (BoW) model; and Gray Level Co-occurrence Matrix (GLCM). They found that BoW outperforms low-level feature extraction methods while costing the overall complexity of the model on a higher side. The Deep Convolutional Neural Network (DCNN) is often used for analyzing images with minimum preprocessing. [PERSON] [11] introduced the deep neural network \"lenet\" in text-visual applications. Through state-of-the-art studies, [PERSON] et al. [12] explained that handcrafted features might be replaced by automatic feature extraction in intensive learning approaches. [PERSON] et al. [13] claimed 96.15% accuracy for context-based image retrieval using CNN for CE-MRI dataset with transfer learning for VGG-19 architecture. He used a strategy to fine-tune the retrieval performance and used Closed-Form Metric Learning (CFML) to compare the database and query images. [PERSON] et al. [14] used the superpixel technique, and their method classified each superpixel. They ensured robust classification by introducing a number of novel image features extracted from each superpixel viz. intensity-based, Gabor textons, fractal analysis, and curvatures. The binary classification, based on tumor and non-tumor classes, was carried out using extremely randomized trees (ERT) classifier and SVM. [PERSON] et al. [15] introduced a 3D supervoxel based learning system for tumor segmentation in multimodal MRI brain images. For each supervoxel, the extracted first-order intensity statistical features are fed to a random forest (RF) classifier to categorize each supervoxel into tumor core, edema, and healthy brain tissue. [PERSON] et al. [16] carried out automated segmentation of brain tumors in multimodal MRI images by integrating machine-learned features, using fully convolutional networks (FCN), and handcrafted features using texton based histograms. They categorized the MRI image voxels into normal tissues and tumors' parts by using an RF classifier. [PERSON] et al. [17] segmented 3 DMRI for brain tumors using multiple encoders and improved the feature extraction process. They introduced Categorical Dice (CD) as a loss function to reduce the volume imbalance problem by setting dissimilar weights for different regions simultaneously. [PERSON] et al. [18] proposed a multi-task deep learning system merging a fusion unit with varying depths for brain tumor segmentation. They used a distance-transform decoder module for the volumetric network (VNet), sharpening the segmentation contours and reducing the generation of rough boundaries. [PERSON] et al. [19] introduced a data segmentation framework for prostate MRI using preprocessed quality enhanced images, with bicubic interpolation, fed to an improved 3D V-NET (3D PBV-Net) based on 3D-convolution, resulting in an excellent segmentation relying less on manual segmentation. Similarly, [PERSON] et al. [20] presented a CNN constituted of three sub-networks (viz.