Viva Voce B13.pptx

Novel Diagnostic Pipeline for Corona Virus Disease
(Covid -19) using Squeeze Net Deep Neural Network
Guided by Presented by
Mrs.B.K.Hemalatha B.Muthu Meenakshi (910717106031)
M.E., D.S.Nandhini (910717106032)
Assistant Professor V.K.Priyanga Shri (910717106038)
ECE S.Shamim Banu (910717106047)
KLNCIT
1
K.L.N.C.I.T /ECE Viva Voce 9/8/2021

Overview
• Objective
• Abstract
• Introduction
• Literature Survey
• Existing System
• Proposed System
• Block diagram
• Hardware/Software Requirements
• Performance Metrics
• Results
• Comparison Table
• Advantages
• Conclusion
• References
2

Objective
• To conduct the automated detection of lung infections from computed
tomography (CT) images using Squeeze Net deep neural network
offers a great potential to augment the traditional healthcare strategy
for tackling COVID-19 and to measure performance metrics such as
accuracy, sensitivity and specificity
K.L.N.C.I.T /ECE Viva Voce 9/8/2021 3

Abstract
• The coronavirus disease 2019 (COVID-19) has become a global pandemic since the beginning
of 2020.
• The disease has been regarded as a Public Health Emergency of International Concern (PHEIC)
by the World Health Organization (WHO) and the end of January 2020.
• Automated detection of lung infections from computed tomography (CT) images offers a great
potential to augment the traditional healthcare strategy for tackling COVID-19.
• However, segmenting infected regions from CT slices faces several challenges, including high
variation in infection characteristics, and low intensity contrast between infections and normal
tissues.
• Further, collecting a large amount of data is impractical within a short time period, inhibiting the
training of a deep model.
• To address these challenges, a novel COVID-19 Lung Infection Segmentation SqueezeNet
which is a convolutional neural network algorithm is proposed to automatically identify infected
regions from chest CT slices.
• In CNN, a parallel partial decoder is used to aggregate the high-level features and generate a
global map.
4

Introduction
• The coronavirus disease 2019 (COVID-19) has become a global
pandemic since the beginning of 2020
• Up to April 10, 2020, there have been more than 1.5 million cases of
COVID- 19 reported globally, with more than 92 thousands deaths
• The most common symptoms of COVID-19 patients include fever, cough
and shortness of breath, and the patients typically suffer from pneumonia.
• Computed Tomography (CT) imaging plays a vital role for detection of
manifestations in the lung associated with COVID-19 , where
segmentation of the infection lesions from CT scans is important for
quantitative measurement of the disease progression in accurate
diagnosis and follow-up assessment
5

Literature Survey
SI.NO TITLE OF THE PAPER AND
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHO
D
INFERENCE
1. “Diagnosis of Corona virus
disease 2019( covid-19) with
structured latent multi-view
representation learning”, IEEE
Transactions on Medical Imaging
Vol 39,no.8.
Feng Shi,
Changqing Zhang
and Dinggang Shen
Year: August,2020
Structured Latent Multi-
view Representation
Learning
Investigating
multiple features
describing CT
images from
different views, a
unified latent
representation is
learned which can
completely
encode
information from
different aspects
of features.

Contd….
NAME OF THE JOURNAL
AUTHORS/YEAR ALGORITHM/METHOD INFERENCE
2. “Lung infection quantification
of covid-19 in CT images with
deep learning”, IEEE
transactions on Computer
vision and Pattern
Recognition(cs.CV):Image
processing and quantitative
methods(q-bio.QM).
F.Shan et al,
Fei Shan, Yaozong
Gao, Yuxin Shi
Year: Mar 2020
Deep-learning(DL)-
based segmentation
system is developed to
automatically quantify
infection regions of
interest and their
volumetric ratios with
respect to lungs
For fast manual
delineation of
training samples
and possible
manual
intervention of
automatic results,
CT scans and
infection
distributions in the
lobes are correlated
well
7

Contd….
NAME OF THE JOURNAL
3. “CPM-Nets: Cross partial multi-
view networks”, Dept: Advances
in Neural Information
Processing Systems 32(NeurIPS
2019)
Changquing Zhang,
Zongbo Han, Yajie
cui, Huazhu Fu
Year: 2019
Proposed a novel
framework termed
CPM-Nets, this
framework give a
formal definition of
completeness and
versatility for Multiview
representation
According to view-
missing patterns,
model fully
exploits all samples
and all views to
produce structured
representation for
interpretability

Contd….
NAME OF THE JOURNAL
4. “Learning to segment skin
lesions from noisy annotations”,
IEEE based Computer Vision
and Pattern Recognition(cs.CV)
Ghassan
Hamarneh, Zahra
Mirikharaji, Yiqi Yan
Year: June 2019
Propose a spatially
adaptive reweighting
approach to treat clean
and noisy pixel-level
annotations
commensurately in the
loss function
Deploy a meta-
learning approach
to assign higher
importance to
pixels whose loss
gradient direction
is closer to those of
clean data

Contd….
NAME OF THE JOURNAL
5. “Deep learning for chest
radiograph diagnosis: A
retrospective comparison of
the CheXNeXt algorithm to
practicing radiologists”,PLOS
Med.,vol .15 no.11
Pranav Rajpurkar,
Jeremy Irvin, Robyn
L.Ball, Hershel
Mehta
Year: Nov 2018
“Deep learning for chest
radiograph diagnosis: A
retrospective
comparison of the
CheXNeXt algorithm to
practicing radiologists”
“Deep learning for
chest radiograph
diagnosis: A
retrospective
comparison of the
CheXNeXt
algorithm to
practicing
radiologists”
10

Contd…
NAME OF THE JOURNAL
6. “Focal Dice loss and image
dilation for brain tumor
segmentation”, Lecture
Notes in Computer Science
book Series(LNCS, Vol
11045)
Pei Wang, Albert C,
S. Chung
Year: 20 Sep, 2018
Proposed a Focal Dice
Loss (FDL) method to
consider the imbalance
among structures of
interest instead of the
entire image including
background.
Image dilation
is applied to the
training
samples, which
enlarges the tiny
sub-regions,
bridges the
disconnected
pieces of tumor
structures and
promotes
understanding
on overall tumor
rather than
complex details
11

Contd….
NAME OF THE JOURNAL
7. “Multi-view feature selection
and classification for
Alzheimer’s disease
diagnosis”, Multimedia tools
Appl .,Vol.76, no.8
M.Zhang, Y. Yang,
F.Shen, Y. Wang
Year: April 2017
Propose a novel multi-
view classification
method based on l2,p-
norm regularization for
Alzheimer’s disease
diagnosis
Investigated and
experimentally
demonstrated that
this method
enhances the
performance of
disease status
classification,
comparing to the
state-of-the-arts
methods

Contd….
NAME OF THE JOURNAL
8. “Automatic detection and
classification of colorectal
polyps by transferring low-
level CNN features from
nonmedical domain”, IEEE J.
Biomedical. Health
Information,Vol.21, no.1
Ruikai Zhang, Yali
Zheng, Wing Chung
Year: Jan 2017
Propose a fully
automatic algorithm to
detect and classify
hyperplastic and
adenomatous polyps.
Proposed method
identified polyp
images from non-
polyp images in
the beginning
followed by
predicting the
polyp histology

Contd….
NAME OF THE JOURNAL
9. “V-Net: Fully convolutional
neural networks for
volumetric medical image
segmentation”, in Proc.
Fourth Int. Conf .3D
Vis.(3DV).
F.Milletari,
N.Navab,and S.-
A.Ahmadi
Year: Oct 2016
Propose an approach to
3D image segmentation
based on a volumetric,
fully convolutional,
neural network
CNN is trained
end-to-end on MRI
volumes depicting
prostate, and learns
to predict
segmentation for
the whole volume
at once

Contd….
NAME OF THE JOURNAL
10. “U-Net: Convolutional
networks for biomedical
image segmentation”, Dept:
Computer Vision and Pattern
Recognition (cs.CV)
O.Ronne berger,
P.Fischer, T.Brox
Year: May 2015
Present a network and
training strategy that
relies on the strong use
of data augmentation to
use the available
annotated samples more
efficiently.
The architecture
consists of a
contracting path to
capture context and
a symmetric
expanding path that
enables precise
localization

Contd….
NAME OF THE JOURNAL
11. “Visualizing data using t-SNE”,
J.Mach.Learn.Res., Vol .9,pp.
2579-2605
Laurens van der
Maaten, Geoffrey
Hinton
Year: 2008
t- SNE is that visualizes
high-dimensional data
by given each datapoint
in location in a two or
three dimensional
map,they are
significantly better than
those product by other
techniques on almost all
of the data sets.
For visualizing the
structure of very
large data sets, we
show how t-SNE
can use random
walks on
neighbourhood
graphs to allow the
implicit structure
of all of the data to
influence the way
in which a subset
of data is
displayed.

Existing system
• ML-Machine Learning
• CNN-Convolutional Neural Network
• Deep Network (Inf-Net)

Machine Learning(ML)
• Machine learning is the study of computer algorithms that improve
automatically through experience and by the use of data. It is seen as a
part of artificial intelligence. ML approaches are divided into three
broad categories
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

Convolutional Neural Network(CNN)
• Convolutional Neural Network(CNN or ConvNet) is a class of deep
neural networks, most commonly applied to analysing visual imagery
• The term “Convolutional Neural Network” indicates that the network
indicates that the network employs a mathematical operation called
convolution. CNN is a type of artificial neural network used in image
recognition and processing that is specifically designed to process
pixel data.

Deep Neural Network(Inf-Net)
• A Deep neural network (DNN) is an artificial neural
network (ANN) with multiple layers between the input and output
layers.
• DNNs are typically feedforward networks in which data flows from
the input layer to the output layer without looping back. At first, the
DNN creates a map of virtual neurons and assigns random numerical
values, or "weights", to connections between them. The weights and
inputs are multiplied and return an output between 0 and 1. If the
network did not accurately recognize a particular pattern, an algorithm
would adjust the weights.

Proposed system
• A novel COVID-19 optimized Lung Infection Segmentation Deep
Network (Squeeze Net) is proposed to automatically identify infected
regions from chest CT slices.

Block diagram
Preprocessing
Feature
extraction/
segmentation
Classification
Result
Input
image
Squeeze
net
Resnet

Modules
• Preprocessing
• Filter
• Feature extraction
• Classification
• Analysis

Input Image
• In this proposed model, we are giving CT images as an input to the preprocessing
block. Data set used to input are referred from “COVID-19 CT segmentation
dataset,” https://medicalsegmentation.com/covid19/, accessed: 2020-04-11.

Preprocessing
• RGB to gray
• Image resizing

RGB to Gray:
An RGB image can be viewed as three images (a red scale image,a green
scale image and blue scale image) stacked on top of each other. In MATLAB, an
RGB image is basically a M*N*3 array of color pixel, Where each color pixel is a
triplet which corresponds to red, blue and green color component of RGB image at a
specified spatial location. Similarly, A Gray scale image can be viewed as a single
layer image In MATLAB, a Gray scale image is basically M*N array whose values
have been scaled to represent intensities.

Image Resizing:
• Image Resizing is necessary when we need to increase/ decrease the
total number of pixels, whereas remapping can occur when we are
correcting for lens distortion or rotating an image. As, Neural
networks receive inputs of the same size, all images need to be resized
to a pixel size before inputting them to the CNN.
27

Filter
• Median filtering is used to remove the noises present in images
• The Median filter is a non-linear digital filtering technique, often used
to remove noise from an image or signal. Such noise reduction is a
typical pre-processing (for eg: Edge detection on an image).
28

Feature extraction
The key concept of the low and high level features extracted from input
images. Low level features include edges and blobs, and high level
features include objects and events. Low level feature extraction is
based on signal/image processing techniques. While the high level
feature extraction is based on machine learning techniques.
29

SqueezeNet
What is SqueezeNet?
a deep convolutional neural network (CNN)
compressed architecture design
model contains relatively small amount of parameters
achieve AlexNet-level accuracy on ImageNet dataset with 50x fewer
parameters
Three advantages of small CNN architectures:
require less communication across servers during distributed training.
require less bandwidth to export a new model from the cloud.
more feasible to deploy on customized hardware with limited memory.

SqueezeNet
Conventional 3x3 rfilter or other replaced by 1x1 convolution filters
1x1 filter has 9X fewer parameters than a 3x3 filter
Fewer inputs to conv layers result in fewer parameters achieved by
using only 1x1 filters prior to the 3x3 conv layer called the squeeze
layer (description in next section)total number of parameters in 3x3
conv layer = (number of input channels) (number of filters) (3*3)

SqueezeNet Architecture

SqueezeNet Architecture
Layers breakdown
layer 1: regular convolution layer
layer 2-9: fire module (squeeze + expand layer)
layer 10: regular convolution layer
layer 11: softmax layer
Architecure specifications
gradually increase number of filters per fire module
max-pooling with stride of 2 after layer 1,4,8
average-pooling after layer 10
delayed downsampling with pooling layers
33

What is the Fire Module?
• building block used in the
SqueezeNet
• Employs Strategies 1, 2, and 3
• Comprised of squeeze layers which
have only 1x1 filters (strategy 1)
• Comprised of expand layers which
have a mix of 1x1 and 3x3
convolution filters
• Number of filters in squeeze layer
must be less than the expand layer
(strategy 2)

ResNet
• ResNet - Residual Networks is a classic neural network used as a
backbone for many computer vision tasks. This model was the winner
of ImageNet challenge in 2015. The fundamental breakthrough
with ResNet was it allowed us to train extremely deep neural networks
with 150+layers successfully. The basic block diagram of Residual
block is shown as below
35

Hardware/Software Requirements
SOFTWARE REQUIRED: MATLAB 2014 a
MATLAB supports standard data and image formats, including JPEG, JPEG-2000,
TIFF, PNG, HDF, HDF-EOS, FITS, Microsoft® Excel®, ASCII, and binary files. It also supports the
multiband image formats BIP and BIL, as used by LANDSAT for example. Low-level I/O and
memory mapping functions enable you to develop custom routines for working with any data format.
HARDWARE REQUIRED:
• System : Windows 10 Pro
• Processor : Up to 2.13 GHz
• Memory : Up to 512 MB RAM.
36

Performance Metrics
• Accuracy
• Sensitivity
• Specificity
37

Formulas used
SENSITIVITY:
Sensitivity= True positive / (True positive+ False Negative)
SPECIFICITY:
Specificity= True negative / (True negative+ False positive)
ACCURACY:
Accuracy= 100-sum(prevalence)
Where, Prevalence= (sum1(1)/total) *100
38

Results and discussion
Input image
39

Gray image
40

Preprocessed image
41

Initial contour
42

Segmentation
43

Final mask
44

Segmented output
45

Layer processing
46

Result
47

Comparison table
SI.No Method Sensitivity Specificity Accuracy
1 U-net 0.214 0.867 0.767
2 Dense –net 0.162 0.912 0.812
3 Inf-Net 0.119 0.95 0.887
4 Proposed 0.099 0.972 0.998
48

Advantages
• Higher accuracy
• Less training needed
• High Dimensionality
• Better Specificity and Sensitivity
49

Conclusion
• Deep learning practices are an area where high scientific achievements are
obtained in different scientific fields day by day. One of these fields is medical
practices and studies such as disease detection, disease classification, and location
of the disease are carried out.
• Dataset were performed as input data to the Squeeze Net network using image
processing techniques. The network, achieved higher accuracy. Squeeze Net
structure, which has been used less than other popular deep learning methods in
previous studies, combined with image processing methods, has shown a
successful result.
• In this proposed model, we increased the parameters such as sensitivity, specificity
and accuracy with 0.5000,0.5000, 99.7%. The ultimate aim of this paper is to
increase the success rate rather than referred paper and Thus, it has shown a
successful result. In this proposed model , we ought to train less models for the
faster execution purpose. Apart all these things, our proposed model accuracy is
remarkable.
50

References
1. Hu, H., Li, Q., Zhao, Y., & Zhang, Y. (2020). “Parallel Deep Learning Algorithms with Hybrid Attention
Mechanism for Image Segmentation of Lung Tumors . IEEE Transactions on Industrial Informatics, 1–1”
2. Mansoor .A, Cerrolaza J., Perez, G., Biggs, E., Okada, k., M.G(2019). “ A Generic approach to Lung field
segmentation from Chest Radiography using Deep Space and Shape”.
3. Ngo, L., Cha, J., & Han, J.-H. (2019). “Deep Neural Network Regression for Automated Retinal Layer
Segmentation in Optical Coherence Tomography Images. IEEE Transactions on Image Processing, 1–1”.
4. Saha, M., & Chakraborty, C. (2018). “Her2Net: A Deep Framework for Semantic Segmentation and
Classification of Cell Membranes and Nuclei in Breast Cancer Evaluation. IEEE Transactions on Image Processing,
27(5), 2189–2200”.
5. Dolz, J., Gopinath, K., Yuan, J., Lombaert, H., Desrosiers, C., & Ayed, B(2018). “HyperDense-Net: A hyper-
densely connected CNN for multi-modal I image segmentation. IEEE Transactions on Medical Imaging, 1–1”.
51

References
6. Xing, F., Xie, Y., & Yang, L. (2016). “An Automatic Learning-Based Framework for Robust Nucleus Segmentation.
IEEE Transactions on Medical Imaging, 35(2), 550–566”.
7. Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016). “Brain Tumor Segmentation Using Convolutional Neural Networks
in MRI Images. IEEE Transactions on Medical Imaging, 35(5), 1240–1251”.
8. Ghesu F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J., & Comaniciu, D. (2016). “Marginal Space
Deep Learning: Efficient Architecture for Volumetric Image Parsing. IEEE Transactions on Medical Imaging, 35(5), 1217–
1228”.
9. Y.LeCun, Y.Bengio and G.Hinton, “Deep Learning”, Nature, vol.521,no.7553,pp. 436-444, 2015.
10. L.van der Maaten and G.Hinton, “Visualizing data using t-SNE,” J.Mach.Learn.Res., vol.9,pp.2579-2605,Nov.2008
52

Thank you
53

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