Optimal deep learning model For Classification of Lung Cancer on CT Images

1. Keynote talk
on
Optimal deep learning model For Classification of Lung Cancer
on CT Images
Dr.Sachi Nandan Mohanty
FIE,SMIEEE
sachinandan@ieee.org
1st July 2021
22-08-2021 1

2. Plan of Presentation
•Introduction
•Autoencoder
•Convolution Neural Network
•Related Work
•Problem definition
•Methodology
•Results & Discussion
•Contribution
•References
22-08-2021
2

3. Autoencoder
 Under complete autoencoder
 Autoencoder vs PCA
 Sparse autoencoder
 Denoising autoencoder
 Contractive autoencoder
 Convolution autoencoder
22-08-2021 3

4. 22-08-2021
4
Autoencoder
An autoencoder is a type of ANN used to learn efficient data coding in an unsupervised manner.
The aim of the autoencoder is to learn a representation (encoding) for a set of data.
It acts as a dimensionality reduction, by training the network to ignore signal “noise”
Autoencoders are effectively used for solving many applied problems, like face recognition, to acquiring the
semantic meaning of words
Representation learning
Fig 1: Autoencoder Fig 2: Under complete autoencoder

5. 22-08-2021
5
Expectation
•Sensitive enough to input for accurate reconstruction
•Insensitive enough that it does not memorize or overfit the training data.
Stacked Autoencoder
Deep Autoencoder

6. 22-08-2021
6
Autoencoder vs PCA

7. 22-08-2021 7

8. 22-08-2021 8
What Does PCA do?
Experimental Setup for Dimensionality Reduction with examples of Hand written digits (MNIST)
Total train Images : 60,000
Total test Images: 10,000
Image dimension: 28X28(784)
Dimensionality reduction:784 -> 2
Reconstruction: 784 -> 30
Optimizer used: Adam
Learning rate: 0.0001
Loss Function : Mean Squared Error
Epoch: 100 iteration
Source: G. E. Hilton and R. R. Salakhutdinov: “Reducing the Dimensionality of Data with Neural Networks”, Science, Vol
313, July 2006, pp. 504-507.

9. 22-08-2021 9
Autoencoder convergence to PCA
784-> 2 -> 784
Deep Vs. Shallow autoencoder
784-> 1000->500->250->2->250->500->1000->784
Source: G. E. Hilton and R. R. Salakhutdinov: “Reducing the Dimensionality of Data with Neural Networks”, Science,
Vol 313, July 2006, pp. 504-507.
Deep Autoencoder with Non-Linear Activations

10. 22-08-2021 10
Autoencoder
Original
PCA
Reconstruction from Latent Space

11. 22-08-2021 11
Deep Autoencoder Training
Layer by layer pre training

12. 22-08-2021 12
Convolutional Neural Network
 Convolution
 Linear Time Invariant (LTI) System
 Linear Shift Invariant (LSI) System
 Cross Correlation

13. 22-08-2021 13
CNN Architecture
 CNN Architecture
 Convolution Layer
 Receptive Field
 Nonlinearity
 Pooling
•Color image has 3 dimensions: height, width and depth (depth is the color channels i.e RGB)
• Filter or kernels that will be convolved with the RGB image could also be 3D
• For multiple Kernels: All feature maps obtained from distinct kernels are stacked to get the final output of that layer
3D Convolution Visualization
Red and green boxes are two different featured maps obtained by convolving the
same input with two different kernels. The feature maps are stacked.
along the depth dimension as shown.

14. 22-08-2021 14
• An RGB Image of size
32X32X3
• 10 Kernels of size 5x5x3
• Output featuremap of size
32x32x10
3 D Convolution- Visualization

15. 22-08-2021
15
Nonlinearity
• ReLU is an element wise operation (applied per pixel) and replaces all negative pixel values in the feature map by
zero
Source :Arden Dertat https://towardsdatascience.com/applied-deep-learning-
part-4-convolutional-neural-networks-584bc134c1e2
CNN Architecture

16. 22-08-2021 16
•Replaces the output of a node at certain locations with a summary statistic of nearby locations.
• Spatial Pooling can be of different types: Max, Average, Sum etc.
• Max Pooling report the maximum output within a rectangular neighborhood.
• Pooling helps to make the output approximately invariant to small translation.
• Pooling layers down sample each feature map independently, reducing the height and width, keeping the depth
intact.
• In pooling layer stride and window size needs to be specified
Pooling
Figure below is the result of max pooling using a 2x2 window and stride 2. Each color denotes a different
window. Since both the window size and stride are 2, the windows are not overlapping
3 2 5 6
8 9 5 3
4 4 6 8
1 1 2 1
9 6
4 8
Max pool with 2x2
window with stride = 2
• Pooling reduces the height and the width of the feature map, but the depth remains unchanged as shown in
figure

17. 22-08-2021 17

18. 22-08-2021 18

19. 22-08-2021 19
Feature Extraction Classfication

20. 22-08-2021 20

21. 22-08-2021 21

22. Abstract
•Lungs diseases are a major cause of death and disability in the world[4].
•A Computed Tomography (CT) scan used to find position of tumor and
identified the level of cancer of the body.
•CT scan of lung images was analyzed with the assistance of Optimal Deep Neural Network and Linear
Discernment Analysis (LDA).
•Features extracted from a CT images and then Dimensionality of feature is reduced using LDR to classify lung
nodules as either malignant or benign.
•Most of the individuals (age-32-48yr). Male: 78%, Female :22%
•Results show that the proposed classifier gives the sensitivity of 96.2%, with accuracy of 94.56%.
•16% of the individuals are diagnosed in the early stage when the sickness is generally treatable.
•99.82% affected due to prolong smoking habit.
22-08-2021 22

23. Lung cancer Symptoms, Causes & Diagnosis (WHO, 2011)
•Prolong cough, which is usually dry and doesn’t bring up mucus
•Weight loss, slowly (over month)
•Actual cause unknown
•Mycoplasma pneumonia may developed from bird proteins (such as from exotic birds, chickens or pigeons)
•Grain dust from farming
•Silica dust
•Some drugs can be cause like bleomycin, amiodarone, rituximab
•Genetics(2%)
•Chest X-ray
•CT Scan
•High resolution CT scan
•Lung function test
•Lung biopsy 22-08-2021 23

24. 22-08-2021 24
Limitation of existing classifiers
•In existing techniques, the lung images were captured and subjected to segmentation specifically after which the
SVM classifiers was applied and then the accuracies were measured[23].
•ANN could not predict the sort and shape or size of the tumor and it dealt with a number of pixels which is not
valuable for the earlier detection of the cancer [17].
•NN particularly has profound networks with many hidden layers and capable of modelling complex structure.
However, the training algorithm is again more complex[25].

25. 22-08-2021 25
Experimental Setup
Methodology
Filtering and
contrast
enhancement
phase
Feature
Extraction
Histogram feature
•Variance
•Mean
•Standard Deviation
•Skewness
•Kurtosis
Texture Features
•Energy
•Entropy
•Homogeneity
•Contrast
•Correlations
Wavelet-based
features
Feature reduction:
Linear Discriminate
Analysis
Classification of
Lung CT Images
•Deep belief network
•Restricted Boltzmann Machine

26. 22-08-2021 26

27. 22-08-2021 27
How Linear Discriminate Analysis work

28. 22-08-2021 28
Decision surface
Results

29. 22-08-2021 29

30. 22-08-2021 30
Database Description & Experimental results with validation
•500 recorded lung cancer CT image dataset are used for detection purpose.
•The CT scan images with 1.25mm slice thickness were attained by single breath(see Fig.4).
•The location of nodules was recognized by the radiologist also provided in the dataset

31. 22-08-2021 31
Any Question