【病变检测】SVM糖尿病视网膜病变检测【含GUI Matlab源码 3723期】

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⛄一、糖尿病视网膜病变检测

糖尿病视网膜病变检测是一项通过使用深度学习系统来识别糖尿病患者眼底图像中的视网膜病变的任务。这个任务的目标是根据眼底图像的特征，将其分为不同的病变程度，以帮助医生进行糖尿病患者的早期诊断和治疗。

在这个任务中，通常使用深度学习模型来自动提取眼底图像中的特征，并将其与已知的病变程度进行比较，从而进行分类。这些深度学习模型可以通过训练大量的标记数据来学习糖尿病视网膜病变的特征，并在新的眼底图像上进行预测。

这个任务在医学领域中具有重要的应用价值，因为糖尿病视网膜病变是糖尿病患者最常见的并发症之一，早期的诊断和治疗可以有效地减少病情的进展和视力损失的风险。

⛄二、部分源代码

% Project Title: Automatic Diabetic Retinopathy Detection System

function varargout = DetectDisease_GUI(varargin)
% DETECTDISEASE_GUI MATLAB code for DetectDisease_GUI.fig

gui_Singleton = 1;
gui_State = struct(‘gui_Name’, mfilename, …
‘gui_Singleton’, gui_Singleton, …
‘gui_OpeningFcn’, @DetectDisease_GUI_OpeningFcn, …
‘gui_OutputFcn’, @DetectDisease_GUI_OutputFcn, …
‘gui_LayoutFcn’, [] , …
‘gui_Callback’, []);
if nargin && ischar(varargin{1})
gui_State.gui_Callback = str2func(varargin{1});
end
if nargout
[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
else
gui_mainfcn(gui_State, varargin{:});
end

function DetectDisease_GUI_OpeningFcn(hObject, eventdata, handles, varargin)
% This function has no output args, see OutputFcn.
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% varargin command line arguments to DetectDisease_GUI (see VARARGIN)
% Choose default command line output for DetectDisease_GUI
handles.output = hObject;
ss = ones(300,400);
axes(handles.axes1);
imshow(ss);
axes(handles.axes2);
imshow(ss);
axes(handles.axes3);
imshow(ss);
% Update handles structure
guidata(hObject, handles);
% UIWAIT makes DetectDisease_GUI wait for user response (see UIRESUME)
% uiwait(handles.figure1);
% — Outputs from this function are returned to the command line.
function varargout = DetectDisease_GUI_OutputFcn(hObject, eventdata, handles)
% varargout cell array for returning output args (see VARARGOUT);
% hObject handle to figure
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
% Get default command line output from handles structure
%varargout{1} = handles.output;
% — Executes on button press in pushbutton1.
function pushbutton1_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton1 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)

clc
[filename, pathname] = uigetfile({‘.’;‘.bmp’;'.png’;‘*.gif’}, ‘Pick a Fundus Image File’);
I = imread([pathname,filename]);
I = imresize(I,[512,512]);
I2 = imresize(I,[300,400]);
axes(handles.axes1);
imshow(I2);title(‘Query Image’);
ss = ones(300,400);
axes(handles.axes2);
imshow(ss);
axes(handles.axes3);
imshow(ss);
handles.ImgData1 = I;
guidata(hObject,handles);
% — Executes on button press in pushbutton3.
function pushbutton3_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton3 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
I3 = handles.ImgData1;
I4 = imadjust(I3,stretchlim(I3));
I5 = imresize(I4,[300,400]);
axes(handles.axes2);
imshow(I5);title(’ Contrast Enhanced ');
handles.ImgData2 = I4;
guidata(hObject,handles);
% — Executes on button press in pushbutton4.
function pushbutton4_Callback(hObject, eventdata, handles)
% hObject handle to pushbutton4 (see GCBO)
% eventdata reserved - to be defined in a future version of MATLAB
% handles structure with handles and user data (see GUIDATA)
I6 = handles.ImgData2;
I = I6;
%% Extract Features
[feat_disease, seg_img, Affect] = EvaluateFeatures(I);
I7 = imresize(seg_img,[300,400]);
axes(handles.axes3);
imshow(I7);title(‘Segmented Image’);
%set(handles.edit3,‘string’,Affect);
set(handles.edit5,‘string’,feat_disease(1));
set(handles.edit6,‘string’,feat_disease(2));
set(handles.edit7,‘string’,feat_disease(3));
set(handles.edit8,‘string’,feat_disease(4));
set(handles.edit9,‘string’,feat_disease(5));
set(handles.edit10,‘string’,feat_disease(6));
set(handles.edit11,‘string’,feat_disease(7));
set(handles.edit12,‘string’,feat_disease(8));
set(handles.edit13,‘string’,feat_disease(9));
set(handles.edit14,‘string’,feat_disease(10));
set(handles.edit15,‘string’,feat_disease(11));
set(handles.edit16,‘string’,feat_disease(12));
set(handles.edit17,‘string’,feat_disease(13));
handles.ImgData3 = feat_disease;
handles.ImgData4 = Affect;

⛄三、运行结果

⛄四、matlab版本及参考文献

1 matlab版本
2014a

2 参考文献
[1]袁小昊,郭志波.机器视觉在农作物病害自动检测中的应用研究[J].淮阴工学院学报. 2017,26(03)

3 备注
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