- Brief: Directory involve CT Lung Nodule Detection Scripts, testing accuracy of LUNG NODULE DETECTION using AlexNet convolutional neural network.
- Requirements:
- Python3.6,Python2.7(execute caffe pyscript)
- caffe-windows
- Windows 10
- GPU
-
download LIDC-IDRI Dataset from: https://wiki.cancerimagingarchive.net/display/Public/LIDC-IDRI#dbf22419dbb1415080c3adfd39cdc651
-
place the dataset in directory
./LIDC-IDRI
- make sure python 3.6 is available
- packages :
pip install pydicom opencv-python scikit-image
-
git clone to
.\microsoft-caffe\caffefrom : https://github.com/happynear/caffe-windows -
install and compile Caffe on windows, following steps from
caffe-windowsWindows Setup carefullyhttps://github.com/happynear/caffe-windows/blob/ms/README.md (This step will take a long time)
-
make sure the files in this project under
.\microsoft-caffe\caffestill exist after install Caffe sucessfully (important)
- please remove all
.gitkeepfiles in this project before start your experiment - generate a pickle pointer-file with python3.7:
python .\pyprocessing\loadpath.py - make sure the existence of
\TCIA_METADATA\tcia-diagnosis-data-2012-04-20.csv
-
python .\pyprocessing\start.py(This process will take a long time)
- candidate lung nodules and healthy tissues will categorized under
.\TrainingSet - process files & images will saved under
.\pyprocessing\imageBasket\LPT
-
execute
python .\pyprocessing\label_generate.py(
test.txt train.txt val.txtwill be created for Caffe training) -
copy images training set to caffe :
xcopy .\TrainingSet .\microsoft-caffe\caffe\data\nodulesdetect /e /q -
copy 3 text files
test.txt, train.txt, val.txtunder.\pyprocessingto.\microsoft-caffe\caffe\data\nodulesdetect\
-
go to the directory of Caffe, such as
cd .\microsoft-caffe\caffe\ -
Lmdb validation set :
Build\x64\Release\convert_imageset.exe --shuffle --resize_height=64 --resize_width=64 data\nodulesdetect\ data\nodulesdetect\val.txt data\nodulesdetect\val_lmdb -
Lmdb training set :
Build\x64\Release\convert_imageset.exe --shuffle --resize_height=64 --resize_width=64 data\nodulesdetect\ data\nodulesdetect\train.txt data\nodulesdetect\train_lmdb -
mean binary file :
Build\x64\Release\compute_image_mean.exe data\nodulesdetect\train_lmdb data\nodulesdetect\mean.binaryproto
-
adjust model parameters under
.\microsoft-caffe\caffe\models\noduledetectmt2(important) -
start training :
Build\x64\Release\caffe.exe train --solver=models\noduledetectmt2\solver.prototxt >log\alexnet_noduledetection_round1.log 2>&1(This step will take a long time)
-
make sure the existence of
.\microsoft-caffe\caffe\data\nodulesdetect\labels.txt -
set using of python 2.7
-
start testing : Under directory
.\microsoft-caffe\caffe\and executepython testresult.py -
check result under
.\microsoft-caffe\caffe\data\nodulesdetect\test_re.npy -
analysis : Under directory
.\microsoft-caffe\caffe\and executepython testresult.py display
