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      "description": "# Colorectal-cancer-detection-using-ColoPola-dataset\n\n### Methods\nWe 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.\n\n### ColoPola dataset\nThe dataset consists of 572 slices (specimens) with 20,592 images. There are 284 cancer samples and 288 normal samples.\nThis 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.\n\n![normal](https://github.com/haile493/Colorectal-cancer-detection-using-ColoPola-dataset/blob/main/images/normal.png)\n<p>Fig. 1 36 polarimetric images of normal sample</p>\n\n![cancer](https://github.com/haile493/Colorectal-cancer-detection-using-ColoPola-dataset/blob/main/images/cancer.png)\n<p>Fig. 2 36 polarimetric images of malignant colorectal sample</p>\n\n### Requirements\n- Python &gt;= 3.9\n- Pytorch &gt;= 1.12.0 + cu116\n- numpy &gt;= 1.23.0\n- torchmetrics &gt;= 1.2.1\n- scikit-learn &gt;= 1.1.1\n- albumentations &gt;= 1.2.0\n\n### Quick Start\n1. Download the ColoPola dataset that contains all samples with train.txt and test.txt files\n2. 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.\n3. Install packages in **requirements_short.txt**.\n4. 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\n5. Run **main.py** with pretrained = True or False. Then set the paths for val.txt and test.txt to evaluate the trained models.\n\n### Notes\n- 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.\n- 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.\n\n\n",
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      "description": "# Colorectal-cancer-detection-using-ColoPola-dataset\n\n### Methods\nWe 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.\n\n### ColoPola dataset\nThe dataset consists of 572 slices (specimens) with 20,592 images. There are 284 cancer samples and 288 normal samples.\nThis 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.\n\n![normal](https://github.com/haile493/Colorectal-cancer-detection-using-ColoPola-dataset/blob/main/images/normal.png)\n<p>Fig. 1 36 polarimetric images of normal sample</p>\n\n![cancer](https://github.com/haile493/Colorectal-cancer-detection-using-ColoPola-dataset/blob/main/images/cancer.png)\n<p>Fig. 2 36 polarimetric images of malignant colorectal sample</p>\n\n### Requirements\n- Python &gt;= 3.9\n- Pytorch &gt;= 1.12.0 + cu116\n- numpy &gt;= 1.23.0\n- torchmetrics &gt;= 1.2.1\n- scikit-learn &gt;= 1.1.1\n- albumentations &gt;= 1.2.0\n\n### Quick Start\n1. Download the ColoPola dataset that contains all samples with train.txt and test.txt files\n2. 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.\n3. Install packages in **requirements_short.txt**.\n4. 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\n5. Run **main.py** with pretrained = True or False. Then set the paths for val.txt and test.txt to evaluate the trained models.\n\n### Notes\n- 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.\n- 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.\n\n\n",
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