AlexNet CNN Lung Cancer Nodules Detection

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

1. Preparation

git clone to .\microsoft-caffe\caffe from : https://github.com/happynear/caffe-windows
install and compile Caffe on windows, following steps from caffe-windows Windows Setup carefully

https://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\caffe still exist after install Caffe sucessfully (important)

please remove all .gitkeep files 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

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.txt will 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.txt under .\pyprocessing to .\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