FYP SEM VIII TT1 Presentation.pptx

Lung-Disorder Classification and Detection from Chest-Xray using
GAN’S
Under the guidance of
Prof. Richa Sharma
Project Guide
Department of Information Technology
DJSCE
Mumbai University
2022-23
Abhinav Patel
60003190002
Janmey Patel
60003190029
Hardik Patel
60003190019

DEPT. OF INFORMATION TECHNOLOGY D.J.SANGHVI COLLEGE OF ENGINEERING 2
Name of the project/Thesis
Overview
❑Problem Statement
● Problem
● Motivation & Scope
● Aim and problem definition
❑Literature Review
● Existing Methodologies
● Models and Methodologies
❑System Architecture
❑Implementation & Paper publish Status
❑References

Problem
❑ Major diseases that affect the lungs are
pneumonia, tuberculosis, COPD (chronic
obstructive pulmonary disease), Lung
Cancer and COVIP-19.
❑ Lung diseases are a severe matter of
concern all over the world.
❑ Early diagnosis with the advanced
methods and technologies has become
crucial to help in faster recovery and
improve long-term survival rates.
❑ Damage to the lungs cannot be
reversed. Delayed diagnosis results in
delayed treatment and smoking-cessation
intervention, so early and accurate
diagnosis is a window of opportunity to
make a real difference to a patient's life.

Motivation
❑ The manual analysis of x-ray images is a long
process requiring radiological expertise and a
large volume of time. Deep learning can play a
crucial role in exceeding decision making,
detecting marks of disease as well as conducting
the initial examination and suggesting urgent
cases.
❑ DL techniques are known to require very large
amounts of data to train the neural networks
(NN), which can sometimes be a problem due to
limited data availability.

Scope
❑ Generative Adversarial Network (GAN) is a type of generative model based on deep
neural networks. This technique is known for learning to generate new data with the same
statistics as the training set.
❑ By using GANs to generate synthetic medical data, we will be able to create new datasets
and share them in the medical community.
❑ We believe that hospitals could find this solution useful, especially due to data
confidentiality.
WHY GANS ?
❑ Limited Data Availability
❑ Unlabelled datasets and class imbalance
❑ More sharper and discrete outputs.

Aim
AIM: To develop a hybrid Gan based model to overcome the issues in existing models
and detect lung disorders in the input X-Ray images.
Diseases to be detected:
1. Pneumonia
2. Lung Cancer
Problem Definition: To study the performance of a new hybrid model that can accurately
detect and classify the lung disorders. We believe that the new model will prove to be better
than the existing ones and can be conveniently used in the clinics.

Literature Review
❑ The first CAD system for detecting lung nodules or affected lung cells in the late 1980s but those efforts
were not enough. WHY? Inadequate computational resources for the implementation of advanced
image processing techniques. It is also much time consuming.
❑ A 3D deep CNN is proposed with multiscale prediction strategies in order to detect the lung nodules
from segmented images .However, the work cannot classify disease types and the multiscale prediction
approaches.
❑ A system is built on deep learning based computer aided diagnosis. Deep learning based CAD system
is used for the clinically significant detection of pulmonary masses/nodules on chest X-ray images.
❑ Moreover, deep learning method is also proposed in another paper where several transfer learning
methods are used for pneumonia diagnoses, but the parameter tuning for their implemented methods
are very complex.

Literature Review and Existing System
❑ Lung-GANs: Unsupervised Representation Learning for Lung Disease Classification
Using Chest CT and X-Ray Images
❑ CNN architecture for both models. The proposed architecture is trained and tested using a DL and reinforcement
learning (RL) library called TensorLayer. The base learners are trained using Random Forest and Linear Support
Vector Classification.
❑ Linear SVC and random forest were used as base classifiers. These algorithms use different methods to represent
the knowledge, and thus the hypothesis space is explored from different perspectives. As a result, when their
predictions are combined, the resultant classifier achieves better accuracy than each individual classifier.
❑ The classification performance of the proposed Lung-GANs and three other existing unsupervised methods was
compared and Lung-GANs achieved the highest classification accuracy on all datasets used in this work.
❑ Proved 10.7% more accurate than DCEC (unsupervised deep clustering algorithm that incorporates convolutional
neural networks).
❑ The method achieved 97.6% average accuracy compared to the 93.8% average accuracy obtained by the
autoencoder.
❑ It reported higher accuracy (up to 99.5%) and better sensitivity compared to the existing methods on six different
lung disease datasets. In conclusion, Lung-GANs provide a noteworthy improvement in computer-aided diagnosis
of lung diseases.

❑ A generalized framework for lung Cancer classification based on deep generative
models (Non-Gan Approach)
❑ Two deep learning models - Generative and ResNet50.
❑ RestNet50 is pre-trained on the ImageNet dataset. Certain parameters like the number of iterations and the
learning rate were set in order to begin fine-tuning the CXR lung dataset.
❑ The deep convolutional neural network is used as a classifier model. It can be concluded that deep learning
models can accurately classify XCR lung pictures. Increasing the number of training samples increases the system
accuracy due to the increase of the quality of the learned model. The proposed system’s performance saturates
after about 4000 samples.
❑ The classifier takes 1.2334 s on average to classify a single image using a machine with 13GB RAM.
❑ Proposed framework acquires 98.91% overall detection accuracy, 98.46% accuracy, 97.72% precision.

❑ Combination of GAN and CNN for automatic subcentimeter pulmonary
adenocarcinoma classification - Wang - Quantitative Imaging in Medicine and
Surgery
❑ First they compared and integrated the different GAN techniques. Then designed a CNN for classification.
The results suggested that GAN has the potential to alleviate the data insufficiency problem and to improve
the classification performance of the CNN. CNN model is also convenient for implementation in hospital
diagnostic systems and can promote the development of precision medical care.
❑ They followed the below 4 steps:
Data Collection - collecting datasets, confirming the classifications through surveys.
Data processing - Deciding on the raw data, data augmentation techniques and rescaling.
Gan based image synthesis - Testing the three GAN techniques- wGAN-gp, pix2pix and pgGAN.
CNN classification - Testing and comparing various network structures and deciding on the best.
❑ Observations:
First, the performance of the CNN was unstable and inadequate when only using the raw dataset.
Second, an additional dataset significantly improved the performance of the CNN.
Lastly, the CNN performed best with the dataset generated by the progressive-growing wGAN.

❑ Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2
Model and Edge Computing
❑ Dataset is trained by hybrid class balancing with the help of Oversampling and Proportionate class weights
as the current dataset is in the ratio of 1:3:8 (Viral Pneumonia:COVID-19:Normal).
❑ The class imbalance in the dataset was tackled with a combination of the synthetic minority over-sampling
technique (SMOTE) and weighted class balancing. CNN hyper parameter tuning is used to reduce the
training time.
❑ TL models such as SqueezeNet, VGG19, ResNet50, and MobileNetV2 have accuracy of 97.33 percent, 91.66
percent, 90.33 percent, and 76.00 percent. DL model that was trained from scratch has an accuracy of 92.43
percent.
❑ Support vector machine with local binary pattern (SVM + LBP) and decision tree with histogram of oriented
gradients (DT + HOG) are two feature-based ML classification techniques that have accuracy of 87.98 percent
and 86.87 percent.
❑ The best accuracy comes from a hybrid Inception-ResNet-v2 transfer learning model. Data augmentation and
image enhancement help in improving the accuracy of disease classification tasks. The proposed technique has
a 98.66 percent average accuracy.

Related research
TYPES OF GANS :
There are multiple types of GANs that perform different applications but these are some of the important GANs.
1. Vanilla GAN
2. Conditional Gan (CGAN):
3. Deep Convolutional GAN (DCGAN) : preferable as deep learning networks are implemented.
4. CycleGAN:
5. Style GAN:
6. Super Resolution GAN (SRGAN)

Models and Methodologies
Models and their Accuracies:
Below are some of the models that have been used and implemented by researched specific to
various purposes.
Models Accuracy
VC Net 99.49% (Nodule detection)
ReSNET50 98.91% (Overall Model detection)
Modified Capsnet 63.8% (Non-GAN Data Validation)
RF and SVC 97.6% (Model Testing)
Vanilla RGB 69% (Non-GAN Data Validation)
CNN and VGG 94% (Overall Model detection)

System Architecture

Implementation Status
❑ Tried to understand the working of the GANs by training the
MNIST number dataset.
❑ This dataset consist of 60k entries which is divided into 50
epochs for training the dataset.
❑ A seed is used to produce an image. The discriminator is then
used to classify real images (drawn from the training set) and
fakes images (produced by the generator). The loss is
calculated for each of these models, and the gradients are used
to update the generator and discriminator.
❑ As the number of iterations to train the datasets increases, the
accuracy of the generator increases such that even the
discriminator cannot identify the difference between the
dataset and the synthetically generated images.

❑ We have implemented Deep
Convolutional GAN on the NIH
dataset.
❑ The dataset has 1,12,120 X-Ray
images which were passed through
the network.
❑ A set of candidate images is
considered for training the
network.
❑ Next, we are going to activate the
generative loss functions which
will help us to discriminate the
input image from the sample set.

Paper Publish Status

References
1. Chandra Mani Sharma, Lakshay Goyal, Vijayaraghavan M. Chariar, Navel Sharma, "Lung Disease Classification in CXR Images Using Hybrid
Inception-ResNet-v2 Model and Edge Computing", Journal of Healthcare Engineering, vol. 2022,
https://doi.org/10.1155/2022/9036457
2. Ren, Zeyu, Yudong Zhang, and Shuihua Wang. 2022. "A Hybrid Framework for Lung Cancer Classification" Electronics 11, no. 10: 1614.
https://pubmed.ncbi.nlm.nih.gov/32835077/
3. J. Irvin, P. Rajpurkar, M. Ko, Y. Yu, S. Ciurea-Ilcus, C. Chute, H. Marklund, B. Haghgoo, r Ball, K. Shpanskaya, et al.
https://www.sciencedirect.com/science/article/pii/S2352914820300290#bbib17
4. A.A.A. Setio, A. Traverso, T. de Bel, M.S.N. Berens, C. van den Bogaard, P. Cerello, H. Chen, Q. Dou, M.E. Fantacci, B. Geurts, et al.
https://www.sciencedirect.com/science/article/pii/S2352914820300290#bbib12
5. Y. Gu, X. Lu, L. Yang, B. Zhang, D. Yu, Y. Zhao, L. Gao, L. Wu, T. Zhou
https://www.sciencedirect.com/science/article/pii/S2352914820300290#bib28
6. Chouhan, V.; Singh, S.K.; Khamparia, A.; Gupta, D.; Tiwari, P.; Moreira, C.; Damaševičius, R.; de Albuquerque, V.H.C. A Novel Transfer Learning
Based Approach for Pneumonia Detection in Chest X-ray Images. Appl. Sci. 2020, 10, 559.
https://doi.org/10.3390/app10020559
7. Amjad Khan; Zahid Ansari; Improved VGG-16 Convolutional Neural Network Based Lung Cancer Classification and Identification on Computed
Tomography
https://www.jncet.org/Manuscripts/Volume-11/Issue-2/Vol-11-issue-2-M-01.pdf
8. https://www.researchgate.net/publication/354228470_Lung-
GANs_Unsupervised_Representation_Learning_for_Lung_Disease_Classification_Using_Chest_CT_and_X-Ray_Images

Thank You!!!

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