11_Saloni Malhotra_SummerTraining_PPT.pptx
- 1. Click to edit Master title style 1 BRAIN TUMOUR DETECTION USING DEEP LEARNING SUMMER TRAINING PROJECT SUBMITTED BY: SALONI MALHOTRA ENROLLMENT NO: 01296302719 BRANCH: CSE-E
- 2. Click to edit Master title style 2 CERTIFICATE-DRDO
- 3. Click to edit Master title style 3 About Organisation 3 • The Defence Research and Development Organisation (DRDO) is the premier agency under the Ministry of Defence .DRDO is dedicatedly working towards enhancing self- reliance in Defence Systems and undertakes design and Development leading to production of world class weapons systems and equipment in accordance with the expressed needs. • DRDO is working in various areas of military technology which includes aeronautics ,combat vehicles ,electronic ,instrumentation engineering systems ,missiles ,materials ,advanced computing ,simulation of life sciences. • DRDO has many research and Development Labs spread across the country .The different R & D labs are meant to work on different Projects ,One of the Lab of DRDO is SAG(Scientific Anlaysis Group) which is located in Timarpur,Delhi. • Scientific Analysis Group (SAG) ) was established in 1963.Its primary functions is to evolve new scientific methods for design and analysis of communication systems.SAG is working in the area of Cryptology and Information Security. SAG develops tools and technologies based on contemporary mathematics , computer science, electronics and communication for information security
- 4. Click to edit Master title style 4 Technology Used in Project 4 • Python is open source, interpreted, high level language and provides great approach for object-oriented programming. It is one of the best language used by data scientist for various data science projects/application. • Python provide great functionality to deal with mathematics, statistics and scientific function. It provides great libraries to deals with data science application • Version of Python used in project: Python 3.7.6
- 5. Click to edit Master title style 5 Features of Python 5
- 6. Click to edit Master title style 6 Python Libraries Used In This Project 6 • Pandas:-Pandas is the data manipulation and analysis library of python. It is widely used in data science for data processing and visualization as well. • Matplotlib :-Matplotlib is an amazing visualization library in Python for 2D plots of arrays. It was introduced by John Hunter in the year 2002. Matplotlib consists of several plots like line, bar, scatter, histogram etc. • Seaborn:-Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Seaborn consists of plots like distribution plot, scatter plot, heatmap etc. • ScikitLearn :--Sklearn is Python library for machine learning. Sklearn provides various algorithms and functions that are used in machine learning. Sklearn is built on NumPy, SciPy, and matplotlib. • Keras:-Keras is a great library for building neural networks and modeling. • TensorFlow:- TensorFlow is a popular Python framework for machine learning and deep learning. . It helps in working with artificial neural networks that need to handle multiple data sets
- 7. Click to edit Master title style 7 DEEP LEARNING – INTRO 7 BACK • Deep learning is a subset of machine learning , which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy. • Deep learning architectures such as deep neural networks, recurrent neural networks ,artificial neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, Speech recognisation and in a lot of other fields
- 8. Click to edit Master title style 8 Applications of Deep Learning 8 Chatbots Disease Detection- Healthcare Face Recognition Self Driven Cars Data Analysis Recomendation Systems
- 9. Click to edit Master title style 9 Convolutional Neural Networks A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. The pre- processing required in a ConvNet is much lower as compared to other classification algorithms. • Architecture:-A convolutional neural network consists of an input and an output layer, as well as multiple hidden layers. The hidden layers of a CNN typically consist of a series of convolutional layers that convolve with a multiplication or other dot product. The activation function is commonly a RELU layer, and is subsequently followed by additional convolutions such as pooling layers, fully connected layers and normalization layers, referred to as hidden layers because their inputs and outputs are masked by the activation function and final convolution.
- 10. Click to edit Master title style 10 Convolutional Layer:- This layer is the first layer that is used to extract the various features from the input images. The three elements that enter into the convolution operation are input image, Feature detector(filter) and Feature map. The mathematical operation of convolution is performed between the input image and a filter of a particular size MxM. By sliding the filter over the input image, the dot product is taken between the filter and the parts of the input image with respect to the size of the filter (MxM). The output is termed as the Feature map which gives us information about the image such as the corners and edges. Later, this feature map is fed to other layers to learn several other features of the input image. The most important uses of this layer is to first reducing the size of the image and second to extract the main features which are integral form the image. After we are done with the convolution operation, we apply the rectifier function (Relu) to the convolutional layer inorder to increase non – linearity in our images.
- 11. Click to edit Master title style 11 Pooling Layer :-In most cases, a Convolutional Layer is followed by a Pooling Layer. The primary aim of this layer is to decrease the size of the convolved feature map to reduce the computational costs. Depending upon method used, there are several types of Pooling operations. In Max Pooling, the largest element is taken from feature map. Just like in the convolution step, the creation of the pooled feature map also makes us dispose of unnecessary information or features. In this case, we have lost roughly 75% of the original information found in the feature map since for each 4 pixels in the feature map we ended up with only the maximum value and got rid of the other 3.The other main aim for performing pooling is to include “spatial variance capibility”. They are useful as small changes in the location of the feature in the input detected by the convolutional layer will result in a pooled feature map with the feature in the same location. This capability added by pooling is called the model’s invariance to local translation.
- 12. Click to edit Master title style 12 Flattening:- After finishing the previous two steps, we're supposed to have a pooled feature map by now. As the name of this step implies, we are literally going to flatten our pooled feature map into a column like in the image below. The reason we do this is that we're going to need to insert this data into an fully connected network later on. As you see in the image above, we have multiple pooled feature maps from the previous step. What happens after the flattening step is that you end up with a long vector of input data that you then pass through the artificial neural network to have it processed further. Fully Connected network:- The Fully Connected (FC) layer consists of the weights and biases along with the neurons and is used to connect the neurons between two different layers. These layers are usually placed before the output layer and form the last few layers of a CNN Architecture.
- 13. Click to edit Master title style 13 The flattened output is fed to a feed-forward neural network and backpropagation applied to every iteration of training. Over a series of epochs, the model is able to distinguish between dominating and certain low-level features in images and classify them using the Softmax Classification technique. Dropout:- Usually, when all the features are connected to the FC layer, it can cause overfitting in the training dataset. Overfitting occurs when a particular model works so well on the training data causing a negative impact in the model’s performance when used on a new data. To overcome this problem, a dropout layer is utilised wherein a few neurons are dropped from the neural network during training process resulting in reduced size of the model. On passing a dropout of 0.3, 30% of the nodes are dropped out randomly from the neural network. Fully Connected
- 14. Click to edit Master title style 14 • Padding:- Padding is a term relevant to convolutional neural networks as it refers to the amount of pixels added to an image when it is being processed by the kernel of a CNN. For example, if the padding in a CNN is set to zero, then every pixel value that is added will be of value zero. • Weights:-Each neuron in a neural network computes an output value by applying a specific function to the input values coming from the receptive field in the previous layer. The function that is applied to the input values is determined by a vector of weights and a bias (typically real numbers). Learning, in a neural network, progresses by making iterative adjustments to these biases and weights. Activation function:- Finally, one of the most important parameters of the CNN model is the activation function. They are used to learn and approximate any kind of continuous and complex relationship between variables of the network. In simple words, it decides which information of the model should fire in the forward direction and which ones should not at the end of the network. It adds non-linearity to the network. There are several commonly used activation functions such as the ReLU, Softmax, tanH and the Sigmoid functions. Each of these functions have a specific usage. For a binary classification CNN model, sigmoid and softmax functions are preferred an for a multi-class classification, generally softmax us used.
- 15. Click to edit Master title style 15 Project-Brain Tumour Detection 15 BACK What is A Brain Tumour ? • Brain tumor is an abnormal growth of cells that changes the normal structure and behaviour of brain. Tumors that may form in the skull can grow, put pressure on the brain and adversely affect body health. • Brain tumors can be classified in several different ways. For instance, one of the popular classification types is to classify the brain tumors as benign(non cancerous) and malignant(cancerous) tumors.
- 16. Click to edit Master title style 16 Brain Tumour Detection Using Deep Learning 16 BACK • Recent progress in the field of deep learning has helped the health industry in Medical Imaging for Medical Diagnostic of many diseases • Accurate analysis of MRI Scans need to be done to detect the brain tumor and it can be achieved by using deep learning algorithms like Convolutional Neural network • Deep Learning approach along with Data Augmentation and Image Processing is used to detect Brain Tumour by categorising brain MRI scan images into two parts, one with brain tumour and one with no brain tumour
- 17. Click to edit Master title style 17 17 Problem Statement :- Every year, around 11,700 people are diagnosed with a brain tumor. Proper treatment, planning, and accurate diagnostics should be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI) .Therefore with the help of Deep learning Algo Convolutional neural networks and Image processing of The MRI images, we attempt to detect Brain Tumours if present. • Aim:- Therefore In this Project ,we will be Building Two Models one is A basic CNN Model which is less Accurate and the other one is a Complex CNN Model which is Built on VG16 Architecture and is Far more Accurate than the Simple CNN Model. Both The Models will Classify whether the MRI Scan Images have a tumour or not . • DataSet Used :-In this Project we have used The Brain Tumor Detection , a dataset provided by Kaggle.The dataset is comprised of MRI scan images provided as a subset of photos from a much larger dataset of 3 million manually annotated photos. MRI SCAN IMAGES USED IN DATASET
- 18. Click to edit Master title style 18 Code Snippet-1)Simple CNN Model 18 Step1-Importing the Python Libraries Step2-One Hot Encoding The Target Classes
- 19. Click to edit Master title style 19 19 Step3-Creating Three Lists:-1)Storing the Image Data in Numpy Array Form, 2)List to store Paths of All the Images 3) result list for storing one hot encoded form of target Whether normal or tumour Step4- Resizing All the Images with the standard size of (128,128) with 3 colour channels.
- 20. Click to edit Master title style 20 20 Step5-Spliting the Dataset into Training And Test Dataset Step6-Building The Model with Convolutional Layer, Pooling Layer and Flattening Layer
- 21. Click to edit Master title style 21 Step7-Compiling Model And Printing Model Summary Step 8-Training The Model
- 22. Click to edit Master title style 22 Step 9-Evaluating The Model And Printing The Confusion Matrix
- 23. Click to edit Master title style 23 23 Step 10-Calculating Final Accuracy of Our Model Step 11-Plotting Losses of the Model on Dataset
- 24. Click to edit Master title style 24 Step 12-Testing Our Model and Making Our Predictions on A few Images from our Dataset itself. As seen In the Final Step we are able to correctly detect and predict whether our MRI Scan Images have a tumour or not.But Still as the Accuracy of the model is low ,We are going to build our Second CNN Model which is Built Using VG16 CNN architecture and has A higher Accuracy then the Previous Model which is 92.3%.The Code of the Second Model is included in the report .
- 25. Click to edit Master title style 25 Final Sequence Diagram 25 Normal Image Tumour Image
- 26. Click to edit Master title style 26 26 References • https://www.python.org/ • https://www.coursera.org/ • https://www.udemy.com/machine-learning-a-to-z-/ • https://www.datacamp.com/courses/intro-to-python-for-data- science • https://www.javatpoint.com/deep-learning-algorithms • https://machinelearningmastery.com/a-tour-of-machine- learning-algorithms • https://www.data-flair-training.com/deep-learning/project-ideas • https://www.towardsdatascience.com/convolutional -neural - networks
- 27. Click to edit Master title style 27 Thank You!