A healthy brain can be affected by many diseases. A brain tumor is one of them. As an initial signs of brain tumor its formation is due to the evolution of unusual cells in the brain. The initial signs are specific and general in terms of signs. As the general sign it create pressure on brain or spinal cord, while the specific signs indicate when the specific part of the brain is not working well. In the benign tumor tissues, the growth is sluggish as matched to the malignant tumor. Although it is not metastatic [1]. A malignant tumor is cancerous cells that cause a malignant tumor. The speed of this kind of tumor is quick and destroys the neighboring cells immediately. It is metastatic as compared to a benign tumor. The cure of the brain tumor hinge on upon its type and position [2]. Medical field technology is emerging, and different techniques are used in the medical field for different medical diagnoses. These techniques have helped radiologists to diagnose and detection of tumors [3]. In the case of brain tumors, magnetic resonance imaging (MRI) is the modality of choice. MRI is one of the innovative techniques for imaging in the field of radiology having several sequences (T1, T2 weighted and fluid-attenuated inversion recovery images). It gives a detailed image and an inside view of the brain and other parts [4]. Several methods and algorithms were offered by scholars and researchers. As compared to other algorithms, the use of deep learning is increasing day by day due to its better accuracy results when large data is trained. It can learn and seek the data having dominant attributes step by step. It provides better accuracy results [5].

Deep learning can seek and learn from the data which is disorganized even without any label. For refining the results, this method repeats the tasks again and again through little modification. Deep learning has shown a great influence on problem-solving techniques like the classification of images, processing, and recognition of speech. A neural network having many additional layers is called a deep neural network. If compared to other neural networks, the framework of a deep neural network has contrast due to the existence of many hidden layers. Just like neurons in the human brain deep neural networks also have interrelated components and elements. These elements assign the network's tasks. Several groups are present in the network in the form of layers like input, hidden and output layers. Separately every layer has a link with the other. Weights are assigned to each connection between layers. In other words, we can say that it work as a feed-forward neural network that has several hidden layers [6]. A standout amongst the most well-liked kind of deep neural architecture is acknowledged as convolutional neural networks (CNN). It is one of the prominent deep learning architectures which provide the best accuracy results in supervised and unsupervised classification [7]. Every year almost 11,000 people from the population are diagnosed with a brain tumor. This population includes 500 youngsters and children. Each year 5000 people lost their lives [8]. Its proper diagnosis, detection, and grading with high accuracy are still challenging in the medical and research field as its characteristics vary from patient to patient.

In this research work, three robust automated approach based on CNN, multi-level CNN, ResNet-50 based multi-level CNN model are applied for the classification of tumor and healthy patients by using MRI for attaining good accuracy results and making the right treatment decisions. Real-time data is collected from local hospitals for the experiments.

The key objective of this work is to detect and classify brain tumor-affected patients. For classification purposes, three approaches are used. Following are the contributions of this work:

Collection of normal and brain tumor-affected patient data from local hospitals and online repository.

The reaming paper is prepared as the combination of four sections known as: related work provides most recent work in the field, methodology explores the architecture of proposed model, results and discussion provide the results and finally the conclusion section explains the real purpose of proposed work.

Bhaiya et al. [9] used 3 dimensional MRI images for detecting the tumors. For tumor segmentation, a random forest was applied. For the classification, two properties of the images were used like local binary pattern in three orthogonal planes and histogram of oriented gradients. Kumari [10] proposed a method for classification in which for preprocessing of images principle component analysis was used. In this methodology, three types of neural networks were supervised: back propagation algorithm, learning vector quantization (LVQ), and radial basis function (RBF). The training time of RBF was more as compared to LVQ because more time was spent in LVQ. The support vector machine demonstrated in [11] contained the feature extraction using the gray level scoring matrix. This technique with other kernel functions like radial and linear basis was used for classifying the images on types with an accuracy of 98%. Shree et al. [12] showed a technique for classifying the tumor using MRI through discrete wavelet transform along with a three-dimensional gray level dependence matrix for extracting the features. For minimizing the size of features simulated annealing was used and for removing the over-fitting stratified k-fold cross-validation was used. In work done by Naik et al. [13], preprocessing was done for noise removal. For extracting the coefficient of wavelet, 2-D discrete wavelet transform was used. A probabilistic neural network (PNN) was used for classifying tumors and GLMC was used for feature extraction.

In [14], by using the median filter, the preprocessing of the images was done. Through the rules of a decision tree, the classification was done. A hybrid method is applied for the classification of the images for MRIs from the SICAS Repository. The features of the discrete wavelet transform the specific and required features of the images were extracted [15]. Another approach is used for multi-level deep CNN to classify the images and to maximize the accuracy [16]. Through a genetic algorithm, the features which were extracted were reduced and then the classification of the images was done by using a support vector machine. The combination of two methods like principal component analysis and Gaussian model was proposed for automatically extracting the essential and required features in [17]. By using the real data sets, improved classification results were shown in [18]. For segmenting the tumor region k-means were used and a two-tier method was used for classification. For feature extraction a discrete wavelet transform was used and for training of these features a neural network was used. For training purposes, the k-nearest neighbor was used. Aamir et al. [19] designed a method through creating the pixel matrix, the specific features were obtained using GLCM. Knapsack problem was considered for genetic algorithm implementation. The fitness function was used for checking the solution's quality [20]. Removing the over-fitting decision tree along with random forest was used for better training. Firstly, changing the input MRI images into the grayscale images were done by using matplotlib Library in python. For extracting the required features discrete wavelet transform was applied. The BPNN was used for classification of images. Neural network was adjusted and used for better classification in [21].

Alfonse et al. [22] designed a technique for segmenting and to classify the MRIs of the brain tumor using BWT and SVM. For better detection skull was stripped for getting the high accuracy level. In [23], a SVM was used for classifying the tumor images and for extracting the features fast Fourier transform was used, and for achieving the required features maximal relevancy method was used. In the end accuracy of 98% was achieved. MRI of the brain has two types of regions having normal and abnormal cells. For extracting the desired region of the tumor, these two regions need to be separated for further technique implementation [24]. A new methodology using the techniques of ANN along with features of the gray wolf technique was proposed in [25] for providing better classification results on MRI of the brain. Comparisons were made for analyzing the proposed method with other neural networks and results were provided showing better classification accuracy.

In this section a brief illustration of the technique used for MRI brain tumor classification is presented. The current machine learning methods have the minimum number of kernels and learning parameters and huge time complexity. To handle this problem, a multi-level convolutional neural network is proposed. The proposed model is divided into two levels: One for the detection of tumor and the second level is for the classification of the tumor. The first level uses the 17 CNN layers for detection and the second level uses the 10 CNN layers for the classification of malignant and benign tumor types [26]. A brief summary of the convolutional neural network is presented in Fig. 1.

We have analyzed results through Python language by using supportive libraries; numpy array (np), operating system (os), keras models, layers and utils, matplotlib, sklearn utils and metrics, tensorflow models, pandas, tqdm, and seaborn. These libraries support the development of machine-learning applications. The proposed model was evaluated on the system with an Intel(R) Core i7 7^th generation, Nvidia GeForce GTX 1060 (6 GB) and 16 GB of RAM.

We have proposed a brain tumor recognition technique utilizing a factual methodology on MRI images as the input. The identification results of brain tumor disorder for every MRI image are either it's healthy or tumor affected (Benign, Malignant). In our method, images are preprocessed by converting them into one common format and channel for feature extraction by utilizing CNN architecture. We have worked with three models CNN, multi-level CNN, and multi-level CNN with pre-trained ResNet-50 for detection of brain tumors for contribution in this research. The proposed transfer learning methodology for MRI brain tumor classification provides results concerning different statistical values known as precision (P) 0.94%, recall (R) 0.94%, F1-score (F1-S) 0.93% and accuracy 0.95%. The results reveal that this technique is much better in each constant as compared to the other state-of-the-art techniques. As a future directions the applied research may help the radiologist to accurately diagnosis the brain tumors.