WELCOME
Malay Mitra
Lecturer in Computer Science & Application
Jalpaiguri Polytechnic
West Bengal
A Neural Network Based Intelligent
System for Breast Cancer Diagnosis
Supervised By : Prof. (Dr.) Ranjit Kumar Samanta
Department Of Computer Sc. & Appl.
University Of North Bengal
West Bengal, India.
Why on Breast Cancer
• According to ACS, the new breast cancer
cases are 229,060 in US only in 2012.
• It ranks second as a cause of death in
woman after lung cancer.
• One of the reasons of survival of 2.5
million breast cancer patients in US is
early diagnosis.
Different methods for detecting
Breast Cancer
• Biopsy
Accuracy : 100%
• Mammography
Accuracy :
68% to 79%
• FNAC
Accuracy :
65% to 98%
Points which are noted from
previous studies
• Much of the works having high classification
accuracy are based on hybrid approach.
• NN and SVM with various combinations like LS,
f-score,RS lead to higher classification accuracy.
• From some work it is not clear that the accuracy
they obtained are the result of best simulation
product or average of several simulations.
Wisconsin Breast Cancer Database
Wisconsin Breast Cancer data description with statistics
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Attribute
Sample Code number
Clump thickness
Uniformity of cell size
Uniformity of cell shape
Marginal adhesion
Single epithelial cell size
Bare nuclei
Bland chromatin
Normal nuclei
Mitosis
Class
Domain
Mean
SD
id number
1-10
4.44
2.83
1-10
3.15
3.07
1-10
3.22
2.99
1-10
2.83
2.86
1-10
2.23
2.22
1-10
3.54
3.64
1-10
3.45
2.45
1-10
2.87
3.05
1-10
1.60
1.73
( 2 for benign, 4 for malignant )
Feature Extraction and
Reduction
• CFS (Correlation based Feature Selection)
• AR (Association Rule)
• RS (Rough Set)
Correlation based feature subset
selection
rfc 
Where,
k rfc
{k  k (k  1)rff
rfc
Correlation between summed features and class variable
rfc
Average of the correlation between features
k
Number of features
rff
Average inter-correlation between features
Rough Set
Let there be an information system
I = (U,A) where U be the universe discourse and a nonempty set
and A is a nonempty set of attributes.
For any P
Let X


A, the equivalence relation IND(P) called P-indiscernibility
relation.
U, be the target set and can be approximated by P-lower
(PX) and P-upper (PX) approximation.
Accuracy of the rough set (PX,PX) = |PX| / |PX|
Artificial Neural Network (ANN)
Mc Culloch – Pitts model of a neuron :
Feature
vectors
as
inputs
Feature
weights
as
synapse
Activation
value
Output
as bias
F1
F2
Fn
:
Summing Part
Output Function
Modeling with ANN
Modeling with ANN involves :
i) Designing the network
ii) Training the network
Design of network involves :
i) fixing the number of layers
ii) fixing the number of neurons in each layer
iii) the node function for each neuron
iv) the form of network whether feed-forward or feedback
type
v) the connectivity patterns between the layers and
neurons.
Training phase involves :
i) Adjustments of weights as well as threshold values from a set of training
examples.
Levenberg Marquardt (LM)
algorithm
During the iteration the new configuration of
weights in step k+1 is :-
w(k  1)  w(k )  ( J J  I ) J  (k )
T
Where J
the Jacobian matrix
the adjustable parameter
error vector
1 T
Applications
The schematic view of our system :Wisconsin Breast
Cancer Database
(Original)
Feature extraction
and reduction using
CFS & RS
Classification using
two combinations
Decision space :
2. Benign
4. Malignant
Data Preprocessing
• We completely randomize the dataset
after discarding the records with missing
values.
• There is no outlier in our dataset.
• The dataset is partitioned into three sets1. Training set ( 68% )
2. Validation set ( 16% )
3. Test set ( 16% )
Feature Selection & Extraction
The reduced feature sets after applying CFS & RS
Sr.
No.
Reduced attributes
( CFS )
Reduced attributes
( RS )
1.
Clump thickness
Clump thickness
2.
Uniformity of cell size Uniformity of cell shape
3.
Bare nuclei
Bare nuclei
4.
Bland chromatin
Marginal adhesion
5.
Normal nuclei
Mitosis
Network Architecture
i) This work uses logistic function of the form f(x)=1/(1+ex) in the hidden
& output nodes.
ii) This work uses one input layer, one hidden layer and one output
layer.
iii) Number of neurons in hidden layer is evaluated from the formula
proposed by Goa
s=√(a1m2+a2mn+a3n2+a4m+a5n+a6)+a7
where s : number of neurons, m : number of inputs
n : number of outputs
a1 to a7 are undefined coefficients.
Using LMS, Huang derived a formula as:
s=√(0.43mn+0.12n2+2.54m+0.77n+0.35)+0.51
In this study m=5, n=1 and hence s=5 (after round off)
Modeling Results
• WEKA was used for feature set reduction using CFS.
• RSES was used for feature set reduction using RS.
• The classification algorithm using these two combinations were
implemented in Alyuda NeuroIntelligence.
Types
Classifier
Network Structure
Epochs
(Retrains)
I
HL
O
Number of patterns
Training Validation Testing
CFS+LM
5
5
1
2000(10)
465
109
109
RS+LM
5
5
1
2000(10)
465
109
109
Performance Evaluation Method
As performance measure we compute :
TP  TN
Accuracy 
x100%
TP  FP  TN  FN
TP
Sensitivity 
x100%
TP  FN
TN
Specificit y 
x100%
TN  FP
Experimental Results
Table shows the compiled results of 120 simulations
Methods
Test set (CCR%)
Specificity
Sensitivity
AUC
Highest
(freq)
Lowest
(freq)
Avg.
Highest
(freq)
Lowest
(freq)
Avg.
Highest
(freq)
Lowest
(freq)
Avg.
Highest
(freq)
Lowest
(freq)
Avg.
CFS + LM
100(6)
94.29
(4)
97.45
100
(19)
84.21
(1)
95.28
100
(38)
94.20
(1)
98.53
100
(10)
94(1)
99.27
RS + LM
100(3)
94.33
(1)
97.23
100
(16)
83.87
(1)
94.94
100
(40)
94.42
(1)
98.46
100
(5)
93(1)
99.11
Observations noted
• Out of two methods CFS+LM shows better
performance in terms of CCR, Sensitivity,
Specificity and AUC.
• Our methods provide 100% CCR as the
highest performance which is comparable
to other studies.
• The lowest CCR is 94.29%.
Conclusion
• This work presents here the highest, lowest and average
behavior of the methods used.
• This work provides a better result as compared to the
result obtained from much of the previous studies.
• It is proposed that CFS-derived features set would have
been worthwhile when the final decision is made by
doctors.
• Moreover the highest, lowest and average performance
of a DSS should be judged by a user of the system
before using.
Thank You