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histopathological-images

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Developed a fine-tuned EfficientNetB0 model which is a pre-trained Convolutional Neural Network (CNN) model to train using lungs and colon cancer dataset and classify if the unseen image belonged to benign, adenocarcinoma or squamous cell carcinoma cancer type.

  • Updated Jul 8, 2024
  • Jupyter Notebook

This is the course project of PRML course. In this project, we have implemented various deep learning algorithms like Transfer Learning, CNN and MLP, and some other classification algorithms like Random Forest, LightGBM etc. to classify histopathological images to reduce the human intervention yet providing accurate classification results.

  • Updated Sep 8, 2022
  • Jupyter Notebook

Created an algorithm to identify metastatic cancer in small image patches taken from larger digital pathology scans. The data used for this competition is a slightly modified version of the PatchCamelyon (PCam) benchmark dataset (does not contain duplicates)

  • Updated Feb 11, 2022
  • Jupyter Notebook

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