Research Object Crate for Colorectal-cancer-detection-using-ColoPola-dataset

# Colorectal-cancer-detection-using-ColoPola-dataset ### Methods We trained and tested three models from scratch (CNN, CNN_2 and EfficientFormerV2) and two pretrained models (DenseNet121 and EfficientNetV2-m) to classify the colorectal cancer using the ColoPola dataset. ### ColoPola dataset The dataset consists of 572 slices (specimens) with 20,592 images. There are 284 cancer samples and 288 normal samples. This dataset can download from [Zenodo repository](https://doi.org/10.5281/zenodo.10068018). The lists of samples for training (train+val) set and testing set are provided in this repository. ### Requirements - Python >= 3.9 - Pytorch >= 1.12.0 + cu116 - numpy >= 1.23.0 - torchmetrics >= 1.2.1 - scikit-learn >= 1.1.1 - albumentations >= 1.2.0 ### Quick Start 1. Download the ColoPola dataset that contains all samples with train.txt and test.txt files 2. Split the training set (list of samples in **train.txt**) into train and validation sets at any desired ratio. Keep the testing set (**test.txt**) for evaluating the trained model(s) as unseen data. 3. Install packages in **requirements_short.txt**. 4. Modify **cc_model.py** to select one of three models from scratch and one of two pretrained models. Then make sure the paths for train.txt and val.txt 5. Run **main.py** with pretrained = True or False. Then set the paths for val.txt and test.txt to evaluate the trained models. ### Notes - In this study, the input shape is **(224, 224, 36)** with 36 channels that are 36 polarized images of each slice (see Fig. 1 and 2). Please read **cc_dataset.py** to know how to make the input data. - The architectures of five models are in build_model_2.py for CNN and CNN_2, efficientformer_v2.py for EfficientFormerV2, and pretrained_models.py for DenseNet121 and EfficientNetV2.

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