通过巴氏涂片图像检测宫颈癌

[](https://github.com/rishavpramanik/CervicalFuzzyDistanceEnsemble/actions/workflows/CodeQL.yml) [](https://github.com/rishavpramanik/CervicalFuzzyDistanceEnsemble/actions/workflows/devskim.yml) [](https://paperswithcode.com/sota/image-classification-on-herlev?p=a-fuzzy-distance-based-ensemble-of-deep) [](https://paperswithcode.com/sota/image-classification-on-sipakmed?p=a-fuzzy-distance-based-ensemble-of-deep) # Cervical cancer detection from Pap Smear Images "A fuzzy distance-based ensemble of deep models for cervical cancer detection" published in Computer Methods and Programs in Biomedicine (June 2022), Elsevier ``` @article{pramanik2022fuzzy, title = {A fuzzy distance-based ensemble of deep models for cervical cancer detection}, author={Pramanik, Rishav and Biswas, Momojit and Sen, Shibaprasad and de Souza J{\'u}nior, Luis Antonio and Papa, Jo{\~a}o Paulo and Sarkar, Ram}, journal = {Computer Methods and Programs in Biomedicine}, volume = {219}, pages = {106776}, year = {2022}, issn = {0169-2607}, doi = {10.1016/j.cmpb.2022.106776}, url = {https://www.sciencedirect.com/science/article/pii/S0169260722001626} } ``` **A fuzzy distance-based ensemble of deep models for cervical cancer detection** Find the original paper [here](https://www.sciencedirect.com/science/article/pii/S0169260722001626). <p align="center"> <img src="./pipe.jpg" width="600" title="Overall Pipeline"> </p> # Datasets Links 1. [SIPaKMeD SCI Pap Smear Images](https://www.cs.uoi.gr/~marina/sipakmed.html) 2. [Herlev](http://mde-lab.aegean.gr/index.php/downloads) 3. [Mendeley LBC](https://data.mendeley.com/datasets/zddtpgzv63/4) # Instructions to run the code Required directory structure: (Note: ``train`` and ``test`` contains subfolders representing classes in the dataset.) ``` +-- data | +-- train | | +--class A | | +--class B | | ... | +-- test | | +--class A | | +--class B | | ... +-- main.py ``` 1. Download the repository and install the required packages: ``` pip3 install -r requirements.txt ``` 2. The main file is sufficient to run the experiments. Then, run the code using linux terminal as follows: ``` python3 main.py --data_directory "data" ``` Available arguments: - `--num_epochs`: Number of epochs of training. Default = 70 - `--learning_rate`: Learning Rate. Default = 0.0001 - `--batch_size`: Batch Size. Default = 16 - `--path`: Data Path. Default= './' - `--kfold`: K-Fold, to perform K fold cross validation. Default= 5 3. Please don't forget to edit the above parameters before you start