MATLAB: Which Network can I use for cancer diagnosis

cancerDeep Learning Toolboxneural network analysis

Hello,I am new in MATLAB application and I have some doubts and queries also.Please suggest me.
1> I am using NN toolbox nprtool for cancer diagnosis.I want to do some comparative test using other networks and algorithms.Which are the all networks and algorithms can I use, please suggest and also which tool can I use?
2>nntool also have some networks but I am getting better result using nprtool for BPNN, why so?
3> Nprtool have the option for BPNN,is it possible to use other networks also like wise?if so,then what is the command for that?
4> Does the code for one release of MATLAB varies from other?I am using R2008a.Is it possible se the same come for all releases?
Thanks a lot. Shraboni

Best Answer

Which Network can I use for cancer diagnosis? Asked by Shraboni Mondal on 29 Dec 2012 at 15:30
% Hello,I am new in MATLAB application and I have some doubts and queries
% also.Please suggest me.
% 
% 1> I am using NN toolbox nprtool for cancer diagnosis.I want to do some
% comparative test using other networks and algorithms.Which are the all
% networks and algorithms can I use, please suggest and also which tool can
% I use?
Tool: nprtool
Nets: Either patternnet or the obsolete newpr.
help newpr
doc newpr
help patternnet
doc patternnet
% 2>nntool also have some networks but I am getting better result using % nprtool for BPNN, why so?
The 'pr' indicates created for pattern recognition and classification
% 3> Nprtool have the option for BPNN,is it possible to use other networks % also like wise?if so,then what is the command for that?
Forget it. Only use patternnet or newpr
% 4> Does the code for one release of MATLAB varies from other?
Yes
% I am using R2008a. Is it possible se the same come for all releases?
No. Check the website documentation for BUGS and release changes
Patternnet introduced in 2010. Use newpr. Practice on the 7 sample data sets
InputMatrix: Standardize input matrix with zscore or mapstd
                   [ I N ]  =  size(input)
TargetMatrix: For c classes the target matrix columns should be columns of the c-dimensional unit matrix.
                   [ O  N ]  =  size( target)   % O = c
Classindices: classind = vec2ind(target)
Transfer Functions: { 'tansig' 'logsig' }
Training Function: 'trainscg'
Training: [ net tr ] = ntrain(net,inputs,target);
History: tr = tr
Output: y = net(input);
Assigned classes: assignedclass = vec2ind(y)
Errors: Err = (assignedclass ~= classind)
No. errors: Nerr = sum(Err)
Error Rate: PCTerr = 100*Nerr/N
To obtain separate trn/val/tst errors, use the masks and indices in tr.
Hope this helps.
Thank you for formally accepting my answer.
Greg
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