The document discusses image classification using Convolutional Neural Networks (CNNs), detailing concepts like CNN architecture, design steps, and distinctions from Artificial Neural Networks (ANNs). It highlights applications in computer vision and natural language processing, emphasizing the importance of CNNs in various domains, including medical research. The conclusion underscores the accomplishments and limitations of deep learning in improving tasks like image classification and object detection.

1. Image
Classification
using CNN
Deep Learning
Created by Sumera Hangi
Roll no =06

2. CONTENTS
– INTRODUCTION (core subject)
– INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS
– WHY DID WE USE A CNN?
– STEPS TO DESIGN CNN
– KEY DIFFERENCE BETWEEN ANN & CNN
– ARCHITECTURE OF A CNN FROM INPUT IMAGE TO IMAGE
REGONITION
– APPLICATIONS OF CNN
– CONCLUSION

3. INTRODUCTION (core subject)
– IMAGE PROCESSING is a method to perform some operation on image, in
order to get an enhanced image or extract some useful information from it.
– COMPUTER VISION is a field of computer science that works on enabling
computer to see, identify & process images in the same way that human vision
does, and then provide appropriate output.
– IMAGE CLASSIFICATION is a complex process that may affected by many
factors.
– The objective of image classification is to identify and portray, as a unique gray
level(or color), the feature occurring in an image in terms of the object or type of
land cover these feature actually represent on the ground .
– Image classification is perhaps the most important part of digital image analysis.

4. INTRODUCTION TO CONVOLUTIONAL
NEURAL NETWORKS
– CNNs are basically combination of convolutions followed by feature maps,
subsampling, image features, dense layer of neural network .
– We are using convolutions ,subsampling , feature maps to extract image feature &
dense layer of NN to categorize input image according to image features.
– Applications of CNN are image & video recognition, object recognition, natural
language processing.

6. SO, WHY DID WE USE A CNN?
– In machine learning, a convolutional neural network is a class of deep, feed-
forward artificial neural networks that has successfully been applied to analyzing
visual imagery.
– CNNs use a variation of multilayer perceptron's designed to require minimal
preprocessing. They are also known as shift invariant or space invariant artificial
neural networks (SIANN), based on their shared-weights architecture and
translation invariance characteristics.

7. STEPS TO DESIGN CNN
– Convolution
– Max pooling
– Flattening
– Full Connection

8. CONVOLUTION
– A convolution sweeps the window through images then calculates its input and filter dot product pixel
values. This allows convolution to emphasize the relevant features.

9. IMAGE 1
IMAGE 2

10. IMAGE 3
IMAGE 4

11. RELU
ReLu is a non-linear activation
function that is used in multi-
layer neural networks or deep
neural networks.
This function can be represented
as: where x = an input value
According to equation 1, the
output of ReLu is the maximum
value between zero and the
input value.
We are using Relu on the
convolution to increase non
linearity

12. MAX
POOLING
CNN uses max pooling to
replace output with a max
summary to reduce data size and
processing time.
This allows you to determine
features that produce the highest
impact and reduces the size of
the image by 75% to avoid the
risk of overfitting also get image
extracted features.

13. FLATTENING
Flattening the matrix to get a
single column image based
features

14. FULL CONNECTION
– Neurons of hidden neural network layers are fully connected with all the features
– Activation function determines the value of the output layer
 Softmax
• We use softmax activation function in the output layer of multiclass CNN system.
• It uses multiple classification logistic regression model.
• It calculates the probability distribution of the event over all the different events.
• The range of the output of softmax is 0 to 1 & the sum of all the probabilities will be
equal to one

15. FULL
CONNECTION
Single column fatten data
works as input layer of fully
connected ANN

16. KEY DIFFERENCE BETWEEN
ANN & CNN
– In CNN, neuron of hidden neural network are fully connected .
– In CNN , input feature are connected with all the neurons. But , in ANN , input
features are connected with only those neurons that are having similar values or
pattern.

17. ARCHITECTURE OF A CNN FROM INPUT
IMAGE TO IMAGE REGONITION

18. APPLICATIONS OF CNN
A)COMPUTER VISION1) Face Recognition:
2) Scene Labelling:
3) Image Classification:
4) Action Recognition :

19. CONTD..
5) Human Pose Estimation 6) Document Analysis:

20. B. NATURAL LANGUAGE
PROCESSING
1) Speech Recognition 2) Text Classification

21. CONCLUSION
– Convolutional neural networks (CNNs) have accomplished astonishing
achievements across a variety of domains, including medical research, and an
increasing interest has emerged in radiology.
– Although deep learning has become a dominant method in a variety of complex
tasks such as image classification and object detection, it is not a panacea.
– Being familiar with key concepts and advantages of CNN as well as limitations
of deep learning is essential in order to leverage it in radiology research with the
goal of improving radiologist performance and, eventually, patient care.