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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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Original: https://blog.csdn.net/matlab_dingdang/article/details/127592748
Author: matlab_dingdang
Title: 【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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