- PROJECT TITLE:
AttenBC-Net: A Deep Convolutional Network for Breast Cancer Detection in MRI Images with Explainable AI
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HARDWARE REQUIREMENTS OS-Windows 10 RAM-8GB ROM-More than 100 GB GPU-Yes CPU-1.7 GHz
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SOFTWARE REQUIREMENTS Software name(Python): Version: 3.9.11 (Download link: https://www.python.org/downloads/release/python-376/ ) Click -> Windows x86-64 executable installer.
Software name: PyCharm: Version: 2020.3.3 (Download link: https://www.jetbrains.com/pycharm/download/other.html)
(For installation procedure, please refer the doc “steps to install python.doc”)
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HOW TO RUN Step 1: Loading the project in PYCHARM Open pycharm Go to File, select Open browse the project from your drive and select it. So that the project will get loaded into the Pycharm. For the first time, Pycharm will take some time to load the settings. Please wait if any process is loading on the bottom of the screen. Check the Project Interpreter (File -> Settings -> Project: 157820-> Project Interpreter). If this location “(C:\Users---\AppData\Local\Programs\Python\Python39-64\python.exe) is not presented, then add this ‘python.exe’ from the installed location. In Pycharm Terminal(bottom left), type the comment “pip install -r requirements.txt” Step 2: Run the program and getting the results From 'current project folder' window in pycharm, Open 157820-> Main->GUI.py’ and click run button In GUI window, 1)Enter Training data(%) (eg : 60,70,80,90) or K Value(eg :5,6,7,8) 2) Click START, after some time the result will be displayed 3) Click Run Graph to view the current result graph. [Expected Execution time expected: 15 – 20 minutes] Step 3: Generate the graphs plotted in the paper From 'current project folder' window in pycharm, open ‘157820-> Main->Result_graphs.py’, and click run button.
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IMPORTANT PYTHON FILE AND DESCRIPTION: GUI.py: User Interface, code starts here Main-> Run.py: Main code Main->Preprocessing.py : Preprocessing using pixel enhancement transformation
Main->Run_Resunet.py : cancer region segmentation using ResU-Net with logloss function Main->Img_Aug.py image Augmentation(rotation, random erasing, horizontal flipping, vertical flipping) Main->Fea_Ext.py : Feature extraction using shape features, (Gabor, FLBP, LVP, Statistical features) Proposed_AttenBC_Net ->DCNN.py ,SA_Net.py: Breast cancer detection using AttenBreast cancer deep convolutional Network (AttenBC-Net) from Breast Cancer Deep Convolutional Neural Network (BCDCNN) (paper 1) and SA-Net (shuffle attention network) Explainable_AI-> Explainable_AI.py : explainable AI with Shap algorithm (trained model and features)
Main-> Result_graphs.py: displays graphs in paper. Add initial README.md