My Project : Brain Tumor detection using deep learning

 Project => Brain Tumor detection using deep learning framework tensorflow

Part 1 : Build and train Model using  Tensorflow and keras [CNN]

#Terms

CNN: 

• Convolutional neural network designed for processing structured grid data such as images
• Used: for tasks like => Image Recognition, Image Clssification 
• CNN they can automatically learn hierarchical representation of visual data

        Hierarchical Data : It means  all 5 layers (Components) 

    

       Components of CNN :

            1. Convolutional Layer

            2. Pooling layer

            3. Fully connected layer

            4. Flatten Layer

            5. Output layer

  Advantages of CNN

   1. Automatic Feature Extraction: 

        CNN automatically learn most relevant features from input  data, which is particularly useful for             image and video processing

  2. Hierarchical Learnig : They learn more complex pattern

 Image Classification:

                    process of taking image as input and assigning it to specific category or label 

                    Eg: give image of cat to model it will identify it as cat

     > Real world application:

                 1. Social Media: Automatically tagging friends in photos

                   2. Healthcare : Identify diseases from medical images

                    3. Security: Recognize Faces for Authentication

Modules in our system:

                     1. Preprocessing

                     2. Deep learning model

                     3. Training model                     

                     4. Validation

                   5. Testing

                      6. User interface

AI , ML , DL 

AI: creating machines that performs automation tasks (voice assistant,ChatGPT)

ML: subset of AI,

        focus on creating machine that can learn from data without being explicitly programmed

         (Eg: Spam filter in email)

DL : subset of ML,that uses neural networks with many layers(hence "deep") ro analyze various types            of  data ,especially larger amounts 

     (Eg: A system that can recognize and categorize different objects in a photo,like identifying                      cars,tress, and people

   

AI is the broad concept of machines performing tasks that require intelligence.

ML is a way to achieve AI by allowing machines to learn from data.

DL is a specific type of ML that uses multi-layered neural networks to analyze complex data.     

 Features: 

Edges, textures, shapes,size

Features are the important parts of MRI images that help your model detect brain tumors.

Preprocessing: 

Normalize data, resize data 

#Dataset:

kaggle

3600 images

folder  yes,no(labels) yes:affected,no:not affected brain

#Libraries 

1. OS: Operating System,

2. Numpy :for multidimensional calculations

3. PIL : Image Library ( pillow:Greyscale RGB)

4. CV2 : library for working with image

# Framework 

1. Tensorflow  :  open source library for ML and DL application

2. Flask: web framework in python

3. Keras : High level neural network API

#images

     MRI  Images[Magnetic resonance imaging ]

     MRI images are pictures of human body taken with machine that uses strong magnets and radio       waves   

     .jpg format

      grayscale [RGB]  --- converted using Pillow library

#resized image

64*64 format

#convert to numpy array

100 time faster is numpy array

#reshape data

[2400,64,64,3]

no.of image,size,channels(RGB)

#normalize data (for training purpose)

> from tensorflow import keras

#import data

#build model

        > sequential 

#input shape

#flatten layer

Part 2 : TEST Model

Test that our model works on new data

Part 3 :  Web App using Flask Frame work

# Project Zip File : Available on my mail in Zip File (namely: Brain Tumor Classification DL.zip) 

Question:

What is use of project ?

Ans: Accurate result

    Faster : Domain expert take more time

      Less costlier 

Which Algorithm you used in your project ?

Ans: CNN USED gernerally of image classificstion

Explain CNN Layers