The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey mass, texture, and shape are extracted from the images. Extracted features are trained with deep learning Convolution Neural Network (CNN). The network has been designed with a single convolution layer towards dimensionality reduction. The texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix, which has been transformed into a single-dimensional feature vector at the convolution layer. The neurons of the intermediate layer are designed to measure White Mass Texture Support (WMTS), Gray Mass Texture Support (GMTS), White Mass Covariance Support (WMCS), Gray Mass Covariance Support (GMCS), and Class Texture Adhesive Support (CTAS). In the test phase, the neurons at the intermediate layer compute the support as mentioned above values towards various classes of images. Based on that, the method adds a Multi-Variate Feature Similarity Measure (MVFSM). Based on the importance of MVFSM, the process finds the class of brain image given and produces an efficient result.

Towards detecting an anomaly in brain images, different approaches are discussed in the literature. Features like white mass values and shape features have identified the presence of brain tumors. Various deep learning models like the neural network has been adapted to the problem tumor detection and suffers to meet maximum accuracy in detecting brain tumor. An Adaptive Feature Centric Distribution Similarity Based Anomaly Detection Model with Convolution Neural Network (AFCD-CNN) is sketched towards disease prediction problem to handle the problem. The model considers black-and-white mass features with the distribution of features. First, the method applies the Multi-Hop Neighbor Analysis (MHNA) algorithm in normalizing the brain image. Further, the process uses the Adaptive Mass Determined Segmentation (AMDS) algorithm, which groups the pixels of MRI according to the white and black mass values. The method extracts the ROI with the segmented image and convolves the features with CNN at the training phase. The CNN is designed to convolve the features into one dimension. The output layer neurons are designed to estimate different Feature Distribution Similarity (FDS) values against various features to compute the Anomaly Class Weight (ACW).According to the ACW value, anomaly detection is performed with higher accuracy up to 97% where the time complexity is reduced up to 32 seconds.