【图像识别】基于神经网络实现肺癌图像识别研究附matlab代码

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智能优化算法  神经网络预测雷达通信 无线传感器

信号处理图像处理路径规划元胞自动机无人机

⛄ 内容介绍

随着人工智能的快速发展,将人工智能和临床影像相结合的辅助诊断系统越来越多的被研究,用来减轻医师的工作量, 提高疾病诊断的精确度.本文,将卷积神经网络应用到肺癌病理图像的识别当中,并取得了较好的识别结果,为肺癌智能辅助诊断系统 的开发提供了参考.

⛄ 部分代码

GLCM2 = graycomatrix(d,'Offset',[2 0;0 2]);

c4 = graycoprops(GLCM2,{'contrast','homogeneity','Energy'});

set(handles.edit4,'string',num2str(min(c4.Energy)));

c24= graycoprops(GLCM2,'contrast');

set(handles.edit3,'string',num2str(min(c24.Contrast)));

c5=corr(double(d));

c6=c5(1,:);

c7=c1;

c8=c2;

c9=[c6 c7 c8];

net = network

net.numInputs = 6

net.numLayers = 1

P = size(double(c1));  

Cidx = strcmp('Cancer',c9);

T = size(double(c2));        

net = newff(P,T,25);  

[net,tr] = train(net,P,T);

testInputs = P(:,tr.testInd);

P

testTargets = T(:,tr.testInd);

T

out = round(sim(net,testInputs));

diff = [testTargets - 2*out];

detections = length(find(diff==-1))

false_positives = length(find(diff==1))

true_positives = length(find(diff==0))    

false_alarms = length(find(diff==-2))      

Nt = size(testInputs,2);          

fprintf('Total testing samples: %d\n', Nt);

cm = [detections false_positives; false_alarms true_positives]

cm_p = (cm ./ Nt) .* 100;

view(net);

sim_out = round(sim(net,testInputs));

if ((max(c24.Contrast))>2)

   set(handles.edit1,'string','肺癌');

else

   set(handles.edit1,'string','正常');

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

⛄ 运行结果

⛄ 参考文献

[1]武建国, 王盼, 王娅南. 基于卷积神经网络的肺癌病理图像识别.

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