prathamlearnstocode/patch_tumor_breast_cancer_classification

Designing an Image Classification Algorithm to classify an Image Patch into Malignant and Benign.

Patch_Classification_Tumor

The patches are cropped from the images of DDSM (Digital Database for Screening Mammography) dataset. A regular way to formulate the problem is to first detect the region of interest, and then classify into malignant, and non-malignant. Each of the folders of Training and Validation contains Excel Sheets to indicate the location of the patches (x-min, y-min, x-max, y-max) under consideration. The dataset contains 1855 malignant patches for training and 625 for validation. It contains 3743 non-malignant patches for training and 1232 for validation.

Three models were built -

1. Resnet Model
2. Efficient Model
3. Custom CNN Model

The Model Weights can be accessed from my google drive after a valid permission to use - https://drive.google.com/drive/folders/1T1d67WnpECmrZ5cdTpJAzHm8Lz7WZZ2h Mail me at [email protected] for any doubts.