# Diagnosis of Intracranial Tumors via the Selective CNN Data Modeling Technique
## 2 Related Work  
From the last decade, most of the medical field has been receiving contributions from machine learning and deep learning techniques to make the work easier and more convenient for medical workers. Most of the research papers are oriented toward deep learning techniques, such as CNN's, transfer learning, and classical neural networks for solving problems regarding brain tumors [20; 21; 22]. [PERSON] et al. (2019) performed brain tumor segmentation by using a deep learning algorithm that included a CNN with M-SVM that fragmented the tumor automatically. The MRI images were modified using the Laplacian of Gaussian filtering method (LoG) and contrast limited adaptive histogram equalization (CLAHE). Both methods achieved an accuracy of 84% [23].  
[PERSON] et al. (2020) used a new deep learning technique, i.e., a generative adversarial network (GAN) on the dataset for extracting the robust and structured features from the MRI images, and imported them into a convolution layer architecture to classify three types of tumors: meningioma, glioma, and pituitary tumors. The dataset contained 3064 total images, and they achieved accuracies of 93.01% and 95.6% for the introduced split and random split, respectively [24]. For instance, the simple CNN technique used by [PERSON] et al. (2018) achieved an accuracy of 97.5% in brain tumor detection by using CNN architectures where the weight of neurons was very smaller compared to normal deep learning architectures. They used a gradient descent algorithm for calculating the loss function, and the dataset used was deployed by BRAIN TUMOR IMAGE SEGMENTATION BENCHMARK (BRATS) in 2015 [25].  
The deep neural network was used by [PERSON] et al. (2017) for the classification of brain tumors, where the dataset contained four classes, including sarcoma, glioblastoma, metastatic bronchogenic carcinoma tumors, and normal tumors. The classifiers were used in conjunction with a principal component analysis (PCA) and a discrete wavelet transform (DWT), which yielded fairly good results across all performance metrics, but the dataset used was very small [26]. Similarly, an accuracy of 81% was achieved by [PERSON] et al. (2018). The authors used 3 \(\times\) 3 size filters and the model construction was based on five layers [27]. The results were good, as they had an ensemble of a CNN and a kernel extreme learning machine (KELM), and they named the merging methodology KE-CNN. They compared their proposed methodology with other classifiers, such as an SVM, a radial base function, and some other classifiers.  
A capsule network (CapsNet) was used for tumor detection by recognizing the relationship between the tumor and its nearby tissues. This was a modified form of CNN architecture that was introduced by [PERSON] et al. (2019) [28]. Transfer learning plays an important role in the classification of tumors and was applied to content-based image retrieval (CBIR) by [PERSON] et al. (2019). The evaluation was performed on a publicly available dataset,and [PERSON] et al. (2019) obtained good results [29]. [PERSON] et al. (2019) used the VGG-16 model, which was trained to detect [PERSON]'s disease from MRI images [30].  
Convolutional neural networks greatly contribute to the medical field due to their inclined nature toward images. In the present day, a large amount of unstructured image data are being generated. Therefore, CNN architectures are the best and optimal solution for these real-world problems. In our work, brain MRI images are used, and thus, CNN architecture also plays a crucial role, which is justified in Section 3.4. There are many techniques for detecting brain tumors in patients, but there are also several limitations. The main reason behind this research was to reduce the computational cost and provide an efficient diagnosis system for brain tumors by using CNN. Many radiologists face issues regarding this binary classification due to lack of information and available data.