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Using a trained neural network in app designer for image classification

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Malina Balint
Malina Balint on 1 Apr 2021
Edited: Yogini Prabhu on 25 May 2021 at 13:37
I am trying to build an app with the app designer for skin tumor image classification. I already trained my CNN and I saved it. My problem is that I'm not sure how can I integrate the trained network in my app so that I can classify new images.
The code from the app designer is this:
classdef app3 < matlab.apps.AppBase
% Properties that correspond to app components
properties (Access = public)
UIFigure matlab.ui.Figure
UploadImageButtonPushed_2 matlab.ui.control.Button
AnalyzeImageButtonPushed_2 matlab.ui.control.Button
UIAxes3 matlab.ui.control.UIAxes
UIAxes4 matlab.ui.control.UIAxes
end
properties (Access = private)
filepath
net % neural network
labels1
end
methods (Access = private)
function startupfunc(app)
app.net = load('mini_date2.mat');
app.net = app.net.net;
app.filepath = 'E:/LICENTA/BD_Skin_Cancer_Screening/';
app.filepath = app.filepath.filepath;
end
end
% Callbacks that handle component events
methods (Access = private)
% Button pushed function: AnalyzeImageButtonPushed_2
function AnalyzeImageButtonPushed_2Pushed(app, event)
imds = imageDatastore(app.filepath);
[labels]= classify(app.net,imds);
imshow([labels, imd],'Parent', app.UIAxes4);
end
% Button pushed function: UploadImageButtonPushed_2
function UploadImageButtonPushed_2Pushed(app, event)
[filename, app.filepath] = uigetfile({'*.jpg'},'File Selector');
imagefile = fullfile(app.filepath, filename);
imd = imread(imagefile);
imshow(imd, 'Parent', app.UIAxes3);
end
end
% Component initialization
methods (Access = private)
% Create UIFigure and components
function createComponents(app)
% Create UIFigure and hide until all components are created
app.UIFigure = uifigure('Visible', 'off');
app.UIFigure.Position = [100 100 640 480];
app.UIFigure.Name = 'MATLAB App';
% Create UploadImageButtonPushed_2
app.UploadImageButtonPushed_2 = uibutton(app.UIFigure, 'push');
app.UploadImageButtonPushed_2.ButtonPushedFcn = createCallbackFcn(app, @UploadImageButtonPushed_2Pushed, true);
app.UploadImageButtonPushed_2.Position = [433 322 162 22];
app.UploadImageButtonPushed_2.Text = 'UploadImageButtonPushed';
% Create AnalyzeImageButtonPushed_2
app.AnalyzeImageButtonPushed_2 = uibutton(app.UIFigure, 'push');
app.AnalyzeImageButtonPushed_2.ButtonPushedFcn = createCallbackFcn(app, @AnalyzeImageButtonPushed_2Pushed, true);
app.AnalyzeImageButtonPushed_2.Position = [429 220 170 22];
app.AnalyzeImageButtonPushed_2.Text = ' AnalyzeImageButtonPushed';
% Create UIAxes3
app.UIAxes3 = uiaxes(app.UIFigure);
title(app.UIAxes3, 'Title')
xlabel(app.UIAxes3, 'X')
ylabel(app.UIAxes3, 'Y')
zlabel(app.UIAxes3, 'Z')
app.UIAxes3.PlotBoxAspectRatio = [1.93129770992366 1 1];
app.UIAxes3.ButtonDownFcn = createCallbackFcn(app, @UIAxes3ButtonDown, true);
app.UIAxes3.Position = [48 233 300 185];
% Create UIAxes4
app.UIAxes4 = uiaxes(app.UIFigure);
title(app.UIAxes4, 'Title')
xlabel(app.UIAxes4, 'X')
ylabel(app.UIAxes4, 'Y')
zlabel(app.UIAxes4, 'Z')
app.UIAxes4.PlotBoxAspectRatio = [1.93129770992366 1 1];
app.UIAxes4.Position = [57 36 300 185];
% Show the figure after all components are created
app.UIFigure.Visible = 'on';
end
end
% App creation and deletion
methods (Access = public)
% Construct app
function app = app3
% Create UIFigure and components
createComponents(app)
% Register the app with App Designer
registerApp(app, app.UIFigure)
if nargout == 0
clear app
end
end
% Code that executes before app deletion
function delete(app)
% Delete UIFigure when app is deleted
delete(app.UIFigure)
end
end
end

Accepted Answer

Anshika Chaurasia
Anshika Chaurasia on 19 Apr 2021
  2 Comments
Yogini Prabhu
Yogini Prabhu on 24 May 2021 at 23:56
Hi Anshika
I am aware that this comment is to a specific question by another Matlab client ,
however I wanted to reach to u.
I had posted question about NN, and expected Mr. Greg Heath to answer it, but since I dint get any answers, and here was the only place to find you. please oblige
this is the question: "Please let know how is Cross -validation implemented in the the nprtool (Deep Learning Toolbox)?"
OR
"Please let know how is Cross -validation implemented in the the code of classification of patterns at nprtool (Deep Learning Toolbox)?"
Please respond as soon as possible!
thanking you in advance

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