# A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images
## 1 Introduction
Third, the addition of inadequate signal-to-noise ratio or image distortion usually caused by different factors such as MRI acquisition protocols or variation in imaging devices may further increase the difficulty and influence the precision of final segmentation [16]. The brain tumor detection approaches can be divided into two types, named machine learning (ML)-based [17] and deep learning (DL)-based [18] methods. ML-based techniques mainly include support vector machine [19], conditional random forest [20], decision tree [21], principal component analysis [22], and fuzzy c-means [23]. These techniques require hand-crafted features. The hand-crafted features, here, mean that the features are required to be extracted from training images to start the learning process and perhaps require an expert with extensive knowledge to identify the most important features. Hence, the detection accuracy of the ML-based techniques is dependent on the quality and representation of the extracted features, thus is limited and prone to errors in dealing with large datasets [24]. Meanwhile, DL-based algorithms have shown high performance in various industries including medical imaging [25; 26; 27]. The most common or well-known DL model is the convolutional neural network (CNN) that can instinctively learn dense characteristics directly from the training data due to its weight-sharing nature [28]. Based on these advantages, DL-based brain tumor segmentation has grabbed the researcher's attention [29]. Relevant works include patch-based CNN [30], patch-basedmulti-scale CNN [31], patch-based DCNN [32], fully convolution-based CNN (FCNN) [33], and U-net based [34] brain tumor segmentation models. The patch-based approaches take a small portion of the image as input to CNN and classify each patch into a different class, which degrades the image content and label correlation. The FCNN, on the other hand, is a modified form of CNN, which predicts probability distribution pixel-wise instead of making patch-wise probability distribution predictions [35]. This improvement enables FCNN to take the full-sized image and perform the prediction for the whole image in just a single forward pass. Despite recent advances, the existing DL-based techniques require several convolution layers (CLs) and kernels, increasing the computational cost resultantly. Hence, an efficient method for accurate tumor identification and segmentation with a less complex network in terms of memory and computing resources usage is still in demand [36]. In this paper, we proposed an automated approach for brain tumor detection and segmentation using MRI images. The proposed technique adopts a DL model using a fully convolution neural network, Mask-RCNN [37] with DenseNet-41 backbone, and utilizes a multitask loss function to achieve an end-to-end training of deep CNN, increasing the detection accuracy. The motivation behind using a custom Mask-RCNN was to achieve a similar level of accuracy with a comparatively simple model, fewer kernels, and two convolutional layers. To show the efficiency of the proposed approach, we evaluated our model on free and online available brain tumor datasets [38; 39] using various quantitative measures. The results of brain tumor MRI segmentation are validated through ground truth analysis. The recent works have investigated the effectiveness of a Mask-RCNN model on 3D images of the brain [40] and in other medical fields such as oral disease [41], breast tumor [42], and lung tumor [43] detection and segmentation. The main contributions of the proposed work are as follows:  
1. The proposed method can precisely segment and classify the brain tumors from MRI images under the presence of blurring, noise, and bias field-effect variations in input images. 2. We have created the annotations which are essential for the training of the proposed model because available datasets do not have a bounding box and mask ground truths (GIs). 3.