The document proposes a novel algorithm to diagnose lung cancer using medical images. It involves preprocessing images using a fast non-local mean filter, segmenting images using Masi entropy-based multilevel thresholding with a salp swarm algorithm, extracting features using gray level run length matrix, selecting features with a binary grasshopper optimization algorithm, and classifying images using a hybrid deep neural network with adaptive sine cosine crow search. The algorithm aims to accurately classify lung cancer at early stages, achieve high classification accuracy by optimally extracting features, and minimize classification errors and execution time. It is tested on lung images from publicly available databases and shows improvements over other methods.

1. CERTAIN INVESTIGATIONS ON DIAGNOSIS
OF LUNG CANCER USING NOVEL
ALGORITHM
P.Surendar,
Reg.No: 17134691318
Research Scholar,
Anna University, Chennai.
12/22/2022 1
Supervisor
Dr.M.Ponnibala
Prof/BME
Velalar College of
Engineering and
Technology, Thindal

2. Contents
1. Introduction
2. Literature Review
3. Research Gap
4. Objectives
5. Proposed Work
i. Fast Non Local Mean Filter (FNLM)
ii. Masi entrophy based Multilevel thresholding using Salp
Swarm Algorithm(MasiEMT-SSA)
iii. Feature Extraction using Gray level Run Length
Matrix(GLRLM)
iv. Feature Selection using Binary Grasshopper Optimization
Algorithm)
v. Hybrid Classifier using Deep Neural Network with adaptive
sine cosine crow search) 2

3. Contents
6. Results & Conclusion
7. Future Work
8. References
9. List of Publications
12/22/2022 3

4. Introduction
Medical Image Processing
Image Modalities
Lung Tumor
Tumor Classification
Deep Learning Techniques
12/22/2022 4

5. Medical Image Processing
Image processing
Method to perform few operations on an image,
in order to get an enhanced image or to extract
some information from it.
Medical imaging processing
Creates visual representations of interior parts of
a body.
Introduction
12/22/2022 5

6. Contd…
To reveal internal structures of organs hidden by the
skin and bones, as well as to diagnose and treat
diseases.
Also creates a database of normal anatomy and
physiology to identify abnormalities
Introduction
12/22/2022 6

7. Contd…
Steps in medical image processing:
Preprocessing an image to reduce noise and blur- filtering
Identifying structures within the image - segmentation
Extracting useful information from the image – feature
extraction
Preparing the image for visualization – enhancement
Categorizing the images based on its features - Classification
and Clustering
Introduction
12/22/2022 7

8. Image Modalities
Modalities used for visualizing different structures and
tissues of the human body.
• X – Ray
• Computed Tomography (CT)
• Magnetic Resonance Imaging (MRI)
• Positron Emission Tomography (PET) and
• Ultrasound images
Introduction
12/22/2022 8

9. Image Modalities
• X – Ray
– Produce images of the structures inside human body,
especially bones.
– Used to diagnose the bone fractures; arthritis; infections.
• Computed Tomography (CT)
– CT scans use a series of x-rays to create cross-sections of
the inside of the body, including bones, blood vessels, and
soft tissues.
– Used to diagnose bone fractures; tumors; vascular disease;
heart disease; infections; used to guide biopsies.
Introduction
12/22/2022 9

10. Image Modalities
• Magnetic Resonance Imaging (MRI)
– MRIs use magnetic fields and radio waves to create
detailed images of organs and tissues in the body.
– Used to diagnose stoke; spinal cord disorders; tumors;
blood vessel issues; joint or tendon injuries.
Introduction
12/22/2022 10

11. Image Modalities
• Positron Emission Tomography (PET)
– Use radioactive drugs and a scanning machine to show
how the tissues and organs are functioning.
– Used to diagnose heart disease; coronary artery disease;
seizures; epilepsy;
• Ultrasound images
– Ultrasound uses high-frequency sound waves to produce
images of organs and structures within the body.
– Used to diagnose gallbladder disease; breast lumps;
genital/prostate issues; joint inflammation.
Introduction
12/22/2022 11

12. Filtering
• Medical images are generated by electronic
equipments - presence of noise is inevitable.
• To suppress or remove the noises in an image.
• Filters modify value of pixels by considering the
values of neighboring pixels.
Introduction
12/22/2022 12

13. Image segmentation
• Process of partitioning an image into number of
regions of similar features or segments
Introduction
CT Lung Image and Segmented Image
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14. Image enhancement
• Improve the visibility and perceptibility of specific
regions in an image for analysis.
• Enhancing the contrast among adjacent regions
• Simplifying the image by selective smoothing
Introduction
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15. Lung Tumor
• The tumor is uncontrolled growth of cells in any part of
the body.
• A lung tumor can be either:
– benign or
– malignant
• Not all tumors are cancerous
• Malignant tumor
– very harmful and it spreads to other part of the body.
– have a heterogeneous structure and contain active
cells.
Introduction
12/22/2022 15

16. Contd…
• Benign tumor
– has uniformity in structure and does not contain cancer
cells
– not much dangerous and do not spread to other part of
the body.
Introduction
12/22/2022 16

17. Contd…
• Tumor stages can be identified based on
– tumor size, growth rate or spreading area.
• Staging is a way to describe
– where the tumor is located,
– where it has spread, and whether it is affecting other
parts of the body,
Introduction
12/22/2022 17

18. Tumor Classification
• Tumor classification extracts information from the
segmented tumor region
• Also classifies healthy and tumor tissues for a large
database of medical images.
• Classification algorithm
– Helps to detect and classify the tumors as either benign
or malignant for early diagnosis.
Introduction
12/22/2022 18

19. Deep Learning
• Rapid growth in medical images and modalities requires
extensive efforts by medical expert for analysis.
– Subjective, prone to human error and may have large
variations across different expert.
Introduction
12/22/2022 19

20. Contd…
• Convolutional Neural Networks (CNNs) are biologically
inspired variants of Multi-Layer Perceptrons (MLPs).
• They tend to recognize visual patterns, directly from raw
image pixels.
• DNN is an increase in the number of hidden nodes in a
simple convolution neural network.
Introduction
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21. Contd…
DNN consists of three important layers:
1. Input layer
2. Hidden layer
3. Output layer
Introduction
12/22/2022 21

22. Contd…
1. Input Layer
• This input data specifies the width, height, and number
of channels.
• Typically, the number of channels in CT image is 2 for
each pixel, for color images it is 3 as they are RGB images.
Introduction
12/22/2022 22

23. Literature Review
Literature Review
Author Title Classifier used Accuracy
Supria Suresh et al.
(2019) Journal of
King Saud
University -
Computer and
Information
Sciences
NROI based feature
learning for automated
tumor stage classification
of pulmonary lung
nodules using deep
convolutional neural
networks .
DCNN 97.80
Lakshmanaprabu
, et al (2019)
Future
Generation
Computer
Systems
Optimal deep learning
model for classification
of lung cancer on ct
images, Future Gener
Modified
Gravitational
Search
Algorithm
94.56
12/22/2022 23

24. Contd…
Literature Review
Author Title Classifier used Acccuracy
A.O.de Carvalho
Filho et al. (2018)
Classification of patterns of
benignity and malignancy based
on ct using topology-based
phylogenetic diversity index
and convolutional neural
network, Pattern Recognition
CNN 92.63
Y. Xie et al.
(2018)
Information
Fusion
Fusing texture, shape and deep
model-learned information at
decision level for automated
classification of lung nodules on
chest CT
FUSE-Texture
shape and
data model
89.53
12/22/2022 24

25. Contd…
Literature Review
Author Title Classifier used Accuracy
W. Shen et
al. (2015)
Information
Processing
in Medical
Imaging;
Springer
Multi-scale
convolutional neural
networks for lung
nodule classification,
in: Information
Processing in Medical
Imaging
SVM,RF 86.84
Y. Xie et al.
(2019)
MED
IMAGE
ANALYSIS
Semi-supervised
adversarial model for
benign–malignant
lung nodule
classification on chest
CT
DCNN 92.53
12/22/2022 25

26. Contd…
Literature Review
Author Title Classifier used Accuracy
S.R. Sannasi
Chakravarthy et
al. (2019) Asian
Pacific journal of
cancer
prevention:
APJCP
Lung cancer detection
using probabilistic
neural network with
modified crow-search
algorithm
PNN 90
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27. RESEARCH GAP
• Edge detection and over segmentation is considered as a
research problem in the medical image segmentation.
• Luminance and maintaining structure similarity are the
challenging task in image enhancement.
• The traditional classification algorithms requires a separate
feature extraction model and the Performance gets low for
large volume of dataset.
12/22/2022 27

28. OBJECTIVES
• To find the lung cancer at an early stage
• To present CAD system based hybrid DNN with adaptive
optimization algorithm
• To get better classification accuracy by extracting the
optimal features under the efficient feature extraction and
feature selection approaches.
• To minimize the error during classification process
• To minimize the execution time
12/22/2022 28

29. Simulation Setup
Simulations are performed using
– GPU architecture with MATLAB R2015a.
– Intel Core i3 II generation processor based 4 GB RAM
computer
Dataset
– Lung Image Database Consortium (LIDC) and Image
Database Resource Initiative (IDRI)
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30. Proposed Method
12/22/2022 30
Testing Image
Preprocessing using
FNLM filter
Segmentation by
MasiEMT-SSA
Feature extraction by
GLRLM
Feature selection using
BGOA
Classification using
DNN-ASCCS
Cancerous
Non-Cancerous
Trained Lung
Cancer Images
Preprocessing using
FNLM filter
Segmentation by
MasiEMT-SSA
Feature extraction by
GLRLM
Feature selection using
BGOA
Training Phase
Testing Phase

31. Fast Non Local Mean Filter
FNLM is applied to eliminate the noise from the
images.
neighbourhood filter that mines an average value of
neighbouring pixels to a central pixel
PHASE -I
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32. Fast Non Local Mean Filter
For the NLM, the weights are calculated which only
use pixel information to measure the similarity
between a central region patch and its neighboring
patches.
PHASE -I
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33. Fast Non Local Mean Filter
The proposed method is to combine the advantages
of the Non Local Means algorithm and the bilateral
filter with added texture information as weights.
PHASE -I
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34. Fast Non Local Mean Filter
12/22/2022 34
According to the structural similarity of
neighbouring pixels, the weights are computed.
The high weight is assigned once the similarity of
gray-level in the neighbouring pixel is high.
The FNLM filter is represented as:



I
n
n
I
n
m
w
m
I
NL )
(
)
,
(
)
(
]
[

35. Fast Non Local Mean Filter
12/22/2022 35






n
d
n
I
m
I
G
e
m
Z
n
m
w
2
2
2
)
(
)
(
)
(
)
(
1
)
,
(





36. Masi entropy based Segmentation
Segmentation is a procedure of dividing the image
into sub-regions.
For an image segmentation process, a multilevel
thresholding (MT) method is established that uses
the Masi entropy as an impartial function.
12/22/2022 36

37. Masi entropy based Segmentation
The complexity of MT is growths quickly with
growing number of thresholds. Therefore, the SSA is
employed to adjust the threshold process and to
decrease the computational complexity (CC) involved
in MT.
PHASE -I
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38. Masi entropy based Segmentation
The key motivation of SSA mimics the crawling
behaviour of salps. It is capable to enhance the initial
random solutions efficiently and converge in the
direction of the best value.
PHASE -I
12/22/2022 38

39. Masi entropy based Segmentation
the population is divided into two groups such as
leaders and followers.
In the SSA, the potential solutions are signified by a
salps.
The fitness of each salp is estimated through the
Masientropy function
PHASE -I
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40. Masi entropy based Segmentation
The locations of the salps are the thresholds. By
randomly initializing the locations of the salps, the
algorithm starts in the search space as:
PHASE -I
12/22/2022 40
 
 
lb
lb
ub
noS
rand
round 


 
)
(
.
dim
,

41. Feature Extraction-Grey Level Run Length
Matrix (GLRLM)
The GLRLM approach is utilized to extracting the
higher order numerical texture information.
By re-quantization prior to the growth of the matrix,
the quantity of gray levels is reduced in the image.
PHASE -I
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42. Feature Extraction- GLRLM
The features of Run Length Nonuniformity (RLN), Long Run High
Gray-Level Emphasis (LRHGLE), Low Gray-Level Run Emphasis
(LGLRE), Long Run Emphasis (LRE), Gray-Level Nonuniformity
(GLN), Run Percentage (RP), Long Run Low Gray-Level Emphasis
(LRLGLE),High Gray-Level Run Emphasis (HGLRE), Short Run Low
Gray-Level Emphasis (SRLGLE), Short Run High Gray-Level
Emphasis (SRHGLE), and Short Run Emphasis (SRE) are extracted
using the GLRLM approach.
PHASE -I
12/22/2022 42

43. Feature Extraction-GLRLM
• The GLRLM is established as:
PHASE -I
12/22/2022 43
  max
0
,
0
,
)
,
(
)
( K
v
N
u
v
u
r
K r 



 


44. FEATURE SELECTION-BGOA
Under the specific conditions, the Grasshoppers are
considered a pest. The key feature of these insects is
connected to their movement. In a larval state, its
movement is slow as compared to the huge and
rapid movements of the adults.
PHASE -I
12/22/2022 44

45. Classification-DNN-ASCCS
The structure of DNN contains three layer such as
input layer, output layer and hidden layers .
The DNN is constructed with two hidden layers by
the desired weight
PHASE -I
12/22/2022 45

46. DNN Structure
PHASE -I
12/22/2022 46
Softmax regression
Input
Layer
Hidden
Layer 1
Hidden
Layer 2
Output
Layer

47. Classification-DNN-ASCCS
The combination of sine cosine algorithm and crow
search algorithm is named as SCCS. The benefits of
the two algorithms are considered and use to design
an effective hybrid algorithm which can perform very
well compared to other algorithms.
PHASE -I
12/22/2022 47

48. Classification-DNN-ASCCS
In this work, adaptive SCCS is termed as ASCCS is
introduced for weight updation of DNN. Initially, the
concept behind the CSA is considered in the ASCCS
algorithm
PHASE -I
12/22/2022 48

49. Classification-DNN-ASCCS
The last layer of DNN is a softmax classifier and its
main aim is to categorize the learned features from
DNN. Softmax is used as the activation function in
this classifier.
PHASE -I
12/22/2022 49

50. Filter Output Comparison
12/22/2022 50
(a) (b) (c) (d)
Input Image FNLM Filter Robust bilateral filter Rolling guidance filter

51. Image Segmentation Comparison
PHASE -I
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(a) (b) (c) (d)
a)pre-processed images, (b) segmentation using MasiEMT-SSA, (c)
segmentation using masi entropy (d) segmentation using region growing

52. Comparison of Accuracy Performance
PHASE -I
12/22/2022 52

53. Feature Selection approaches
PHASE -I
12/22/2022 53

54. Lung cancer output images (a) input image (b) filtered
image (c) segmented lung (d) segmented nodule
12/22/2022 54
Input image Filtered image Segmented lung Segmented nodule Types of cancer
Normal
Benign
Malignant
Normal
Malignant
Malignant
Malignant
Normal
Normal
(a) (b) (c) (d)
Benign

55. Confusion Matrix
12/22/2022 55

56. Parameters
The output is either tumor (+ve) or normal (-ve).
• True positive (TP): Prediction is +ve and tumor image
classified as tumor
• True negative (TN): Prediction is -ve and non tumor image
classified as normal
• False positive (FP): Prediction is +ve and tumor image
classified as normal image
• False negative (FN): Prediction is -ve and non tumor
classified as tumor image.
Performance Metrics
12/22/2022 56

57. Contd…
1. Accuracy
• the closeness of a measured value to an actual value.
Performance Metrics
12/22/2022 57

58. Contd…
Accuracy of the proposed lung tumor detection and
classification using DCNN is calculated for:
Three architectures with 128 ×128, 192×192 and
256 × 256 patch sizes.
Performance Metrics
12/22/2022 58

59. Contd…
2. Sensitivity
It is predicting the positive values to actual positive values.
Performance Metrics
12/22/2022 59
FN
TP
TP
y
Sensitivit



60. Contd…
• 3. Specificity:
It is a measurement of predicting actual negatives as
negative.
Performance Metrics
12/22/2022 60
FP
TN
TN
y
Specificit



61. Contd…
The CT images are taken from LIDC (IDRI) and databases.
The dataset consists of 1018 CT scans of 1010 patients.
Here 70 % of the dataset is considered for a training the model and
30% is used for a test set. pixel size ranges between 0.48 and 0.72 mm,
the thickness of each slice varies from 1.25 to 2.5 mm range.
A sample image has been given as an input to the trained model.
This model is able to classify the given image as normal, benign or
malignant.
Performance Metrics
12/22/2022 61

62. Accuracy Performance
12/22/2022 62
Classifier

63. Comparison of Sensitivity
12/22/2022 63
Classifier

64. Specificity Evaluation
12/22/2022 64
Classifier

65. True Positive Rate Performance
12/22/2022 65
Classifier

66. False Positive Rate Performance
PHASE -I
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Classifier

67. Accuracies of different Features
12/22/2022 67

68. Comparison of Accuracy
PHASE -I
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69. Testing and Training Accuracy
PHASE -I
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70. Comparison of Proposed Approach
PHASE -I
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Parameters Acc (%) Sensitivity (%) Specificity (%) TPR FPR
DNN-ASCCS
(proposed)
99.17 99.3 99.03 99.3 0.96
AdaBoost 94.77 93.3 95.7 93.3 4.29
KNN 95.5 94.3 96.3 94.3 3.68
NB 96.3 95.3 97.03 95.3 2.96
Bagging 97.11 96.3 97.64 96.3 2.35
SVM 97.88 97.28 98.28 97.28 1.72

71. Computation Time
PHASE -I
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Processing
Steps
FNLM
Filter
(sec)
MasiEMT-SSA
Segmentation
(sec)
GLRLM
Feature
Extraction
(sec)
BGOA
Feature
Selection
(sec)
Classification
(sec)
Total
Time
(sec)
DNN 1.83 7.14 32.61 50.75 42.23 134.56
DNN-
ASCCS
1.83 7.14 32.61 50.75 15.67 108

72. Comparison of different state of art
methods
PHASE -I
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Authors Classifiers Datasets Accuracy (%)
DNN-ASCCS (proposed) DNN-ASCCS LIDC-IDRI 99.17
Suresh [33] DCNN LIDC-IDRI 97.80
Lakshmanaprabu [35] MGSA LIDC-IDRI 94.56
de Carvalho Filho et al.
[36]
CNN LIDC-IDRI 92.63
Xie et al [37] Fuse-TSD LIDC-IDRI 89.53
Shen et al [38] SVM, RF LIDC-IDRI 86.84
Xie et al [39] DCNN LIDC-IDRI 92.53
Harikumar and Sannasi
[40]
PNN LIDC-IDRI 90

73. Conclusion
Lung cancer is one of the leading cause of cancer mortality
worldwide, with the lowest survival rates after diagnosis.
The early detection of lung tumor helps to increase the
chances of improving patient survival.
Medical image form a vital component of a patient’s health
record and it requires manipulation, processing and
handling of data by computers.
Hence medical data is also a type of bigdata and its
analysis become complex.
In-order to solve these issues DNN-ASCCS classifies the lung
tumor from CT images with high Accuracy.
12/22/2022 73
Results and Conclusion

74. FUTURE WORK
In the future, the proposed approach will be extended by the
diagnosis of lung cancer using advanced methods and algorithms
in less time complexity.
12/22/2022 74

75. MAJOR REFERENCES
1. Arulmurugan, R., and H. Anandakumar. "Early detection of lung cancer using wavelet
feature descriptor and feed forward back propagation neural networks classifier."
In Computational Vision and Bio Inspired Computing, pp. 103-110. Springer, Cham, 2018.
2. Thabsheera, AP Ayshath, T. M. Thasleema, and R. Rajesh. "Lung cancer detection using CT
scan images: A review on various image processing techniques." In Data Analytics and
Learning, pp. 413-419. Springer, Singapore, 2019.
3. Yu, Haixin, Zhong Guan, Katarina Cuk, Yan Zhang, and Hermann Brenner. "Circulating
MicroRNA Biomarkers for Lung Cancer Detection in East Asian Populations." Cancers 11,
no. 3 (2019): 415.
4. Bhattacharjee, Ananya, and SwanirbharMajumder. "Automated Computer-Aided Lung
Cancer Detection System." In Advances in Communication, Devices and Networking, pp.
425-433. Springer, Singapore, 2019.
5. Adir, Yochai, ShovalTirman, Shirley Abramovitch, Cynthia Botbol, Aviv Lutaty,
TaliScheinmann, EyalDavidovits et al. "Novel non-invasive early detection of lung cancer
using liquid immunobiopsy metabolic activity profiles." Cancer Immunology,
Immunotherapy 67, no. 7 (2018): 1135-1146.
12/22/2022 75

76. Contd…
6. Zhang, Yu-Dong &Satapathy, Suresh & Guttery, David &Gorriz, Juan & Wang, Shuihua.
(2021). Improved Breast Cancer Classification Through Combining Graph
Convolutional Network and Convolutional Neural Network. Information Processing
and Management. 58. 102439. 10.1016/j.ipm.2020.102439.
7. Wang, Shuihua&Govindaraj, Vishnuvarthanan&Gorriz, Juan & Zhang, Xin & Zhang,
Yu-Dong. (2020). Covid-19 Classification by FGCNet with Deep Feature Fusion from
Graph Convolutional Network and Convolutional Neural Network. Information
Fusion. 10.1016/j.inffus.2020.10.004.
8. Suresh, Supriya & Mohan, Subaji. (2019). NROI based feature learning for Automated
Tumor Stage Classification of pulmonary lung nodules using Deep Convolutional
Neural Networks. Journal of King Saud University - Computer and Information
Sciences. 10.1016/j.jksuci.2019.11.013
9. Lakshmanaprabu, S., Mohanty, S. N., Shankar, K., Arunkumar, N., & Ramirez, G.
(2019). Optimal deep learning model for classification of lung cancer on ct images.
Future Generation Computer Systems, 92 , 374–382.
MAJOR REFERENCES
12/22/2022 76

77. Contd…
10. de Carvalho Filho, A. O., Silva, A. C., de Paiva, A. C., Nunes, R. A., & Gattass, M.
(2018). Classification of patterns of benignity and malignancy based on ct using
topology-based phylogenetic diversity index and convolutional neural network.
Pattern Recognition, 81 , 200–212
11. Xie, Y., Zhang, J., Xia, Y., Fulham, M., & Zhang, Y. (2018). Fusing texture, shape and
deep model-learned information at decision level for automated classification of lung
nodules on chest ct. Information Fusion, 42 , 102–110
12. Shen, W.; Zhou, M.; Yang, F.; Yang, C.Y.; Tian, J. Multi-scale Convolutional Neural
Networks for Lung Nodule Classification. In Information Processing in Medical
Imaging; Springer: Cham, Switzerland, 2015; pp. 588–599.
13. Y. Xie, J. Zhang, Y. Xia, Semi-supervised adversarial model for benign–malignant lung
nodule classification on chest CT, MED IMAGE ANAL, 57 (2019) 237-248.
14. SannasiChakravarthy, S. R., and HarikumarRajaguru. "Lung Cancer Detection using
Probabilistic Neural Network with modified Crow-Search Algorithm." Asian Pacific
journal of cancer prevention: APJCP 20, no. 7 (2019): 2159.
MAJOR REFERENCES
12/22/2022 77

78. Published
• Surendar.P & Ponnibala.M 2021, ‘Diagnosis of Lung Cancer Using
Hybrid Deep Neural Network with Adaptive Sine Cosine Crow
Search Algorithm’, This research paper was accepted in Journal of
Computational science, Elsevier Netherlands, with DOI:
10.1016/j.jocs.2021.101374, Print ISSN Number : ISSN: 1877-
7503. (Updated Anna University List of Journals (Annexure-I).
12/22/2022 78