[disease detection] realize lung cancer detection system based on BP neural network, including GUI interface

1 brief introduction

With the rapid development of science and technology , Computer technology has been widely used in the field of medical research and Application , among , Computer assisted medical surgery technology is also more valued by people , The application of medical microscopic image processing has attracted much attention . In the past, medical testing was intensive , Low work efficiency , There is subjective observation error ; And it can only record images through photomicrography , Unable to perform necessary processing on the image , It can't store and transmit image information over a long distance through the network quickly . According to the development trend of modern medicine , There is an urgent need for us to display the optical image of traditional technology on the computer screen , And convert it into a large area 、 Images with high brightness that can be automatically recognized , To reduce the heavy labor intensity of professional and technical personnel , Then observe the color of cells 、 Quantitative analysis and statistics of morphology and other parameters , Assist doctors in medical diagnosis . This paper is based on the analysis of the principle of mathematical morphology , The image of lung cancer cells is blurred, unclear and uncertain , Using statistical pattern recognition Fisher Linear decision method , Better processing of lung cancer cell image segmentation . Based on the listed medical basis of lung cancer cell analysis , Focus on the analysis of morphological analysis methods :8- Chain code tracking algorithm and direction code algorithm . At the end of this paper , Extracted lung cancer cells 5 Shape eigenvalues , As the input value of the diagnostic model , Then select samples to test the artificial neural network .

2 Part of the code

​​function varargout = cancer(varargin)gui_Singleton = 1;gui_State = struct('gui_Name',       mfilename, ...                   'gui_Singleton',  gui_Singleton, ...                   'gui_OpeningFcn', @cancer_OpeningFcn, ...                   'gui_OutputFcn',  @cancer_OutputFcn, ...                   'gui_LayoutFcn',  [] , ...                   'gui_Callback',   []);if nargin && ischar(varargin{1})    gui_State.gui_Callback = str2func(varargin{1});t,testInputs)); if ((max(c24.Contrast))>2)    set(handles.edit1,'string','Lung Cancer Affected Image');else    set(handles.edit1,'string','Normal Image');end​​​function edit1_Callback(hObject, eventdata, handles)function edit1_CreateFcn(hObject, eventdata, handles)if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))    set(hObject,'BackgroundColor','white');end​​​function edit2_Callback(hObject, eventdata, handles)​function edit2_CreateFcn(hObject, eventdata, handles)if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))    set(hObject,'BackgroundColor','white');end​​​function edit3_Callback(hObject, eventdata, handles)​function edit3_CreateFcn(hObject, eventdata, handles)if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))    set(hObject,'BackgroundColor','white');end​​​function edit4_Callback(hObject, eventdata, handles)​function edit4_CreateFcn(hObject, eventdata, handles)if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))    set(hObject,'BackgroundColor','white');end​​​function edit5_Callback(hObject, eventdata, handles)​function edit5_CreateFcn(hObject, eventdata, handles)if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))    set(hObject,'BackgroundColor','white');end​​​function edit6_Callback(hObject, eventdata, handles)function edit6_CreateFcn(hObject, eventdata, handles)if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))    set(hObject,'BackgroundColor','white');end​​​function edit7_Callback(hObject, eventdata, handles)​function edit7_CreateFcn(hObject, eventdata, handles)if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))    set(hObject,'BackgroundColor','white');end​

3 Simulation results

4 reference

[1] Li Bo . be based on BP Research on image processing system of lung cancer cells based on Neural Network [D]. Jilin University , 2008.

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