Lung Cancer
Leading cause of millions of deaths
Survivability is high if detected earlier
Difficult to diagnose early
Use of CT images for diagnosis
Non-obstructive image extraction
Current methods detect lung cancer nodules at later stages
There is a need to detect it early
Contributions
First phase
Second Phase
Selection of features using Linear Discriminative Analysis
Classification using Modified Gravitational Search Algorithm
Enhancement and Extraction Methods
Filtering and Enhancement
Feature Extraction
Texture
Wavelet-based
Histograms
Used for noise removal
Prevalent in medical images
Use of filters if neighboring pixels are near 0 or 255
Use of histogram equalization to improve contrast
Plotting through histograms and determine gray levels
Variance
Mean
Standard Deviation
Skewness
Kurtosis
For shape distribution and determines anomalies in images
Determines number of gray level fluctuations
Average gray level in regions
Based on tails
Negative
Positive
Spatial relationship of gray level pixels
Outputs 22 features
Homogeneity
Energy
Entropy
Contrast
Correlations
Maximum values as maximum shape
Quantity of data
For compression
Determines values of minor constrasts
Variance of pixels based on neighbors
Linear dependence of gray levels
Transformation to frequencies through linear transformation
Feature Reduction
LDA
Combines bad features into one
Classification Methods
Deep Belief Networks
Restricted Boltzmann Machine
Modified Gravitational Search Algorithm
For parameter learning
Helps in learning parameters
Stochastic method
For weight optimization
Weight initialization
Force evaluation
Fitness evaluation
Selection of random value
Mass and force updates
Use of maximum specificity
Updates values based on fit, best, and worst values
Note: lacks further explanations on why certain methods were used
Optimal solution and termination
Fine-tuning
Use of backpropagation
Results and Discussion
High accuracy and Specificity
LDA is a better than PCA and ICA
Note: Comparison of performance should be in tabular form than bar charts
S.K., L., Mohanty, S. N., K., S., N., A., and Ramirez, G. Optimal
deep learning model for classification of lung cancer on ct images. Future
Generation Computer Systems 92 (2019), 374–382.