Coders

 Convolution Neural Network: In  deep learning , a  convolutional neural network  ( CNN , or  ConvNet ) is a class of  artificial neural network , most commonly applied to analyze visual imagery. [1]  They are also known as  shift invariant  or  space invariant artificial neural networks  ( SIANN ), based on the shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation  equivariant  responses known as feature maps.  Counter-intuitively, most convolutional neural networks are only  equivariant , as opposed to  invariant , to translation. This is how a general Convolution neural network works internally . But we in this project will create more complex neural network using more number of nodes , which will result in greater no. of filter checks which  will increase the accuracy rate

  The objective of the proposed system is to classify the brain tumor images using :-   1.         convolutional neural network (CNN) 2.         Support vector machine (SVM) 3.         Deep learning models 4.    Image Clustering  5.    Using transfer learning     Images from the dataset are downsized to reduce computation and some salt noise is added to make model robust and the dataset increases.  The classification process undergoes following steps     1- Input   2-    Data Pre-processing   a. Importing libraries – importing libraries b. Data augmentation – modified version of image c. Import the augmented data d. Convert the images to grayscale e. Removal of noise using dilations and erosions and smoothening of images f. Grab the largest contour. g. Find the extreme points of the contoured image h...