Artificial Intelligence (AI) has been used extensively in the field of medicine. More recently, advanced machine learning algorithms have become a big part of oncology as they assist with detection and diagnosis of cancer. Convolutional Neural Networks (CNN) are common in image analysis and they offer great power for detection, diagnosis and staging of cancerous regions in radiology images. Convolutional Neural Networks get more accurate results, and more importantly, need less training data with transfer learning, which is the practice of using pre-trained models and fine-tuning them for specific problems. This paper proposes utilizing transfer learning along with CNNs for staging cancer diagnoses. Randomly initialized CNNs will be compared with CNNs that used transfer learning to determine the extent of improvement that transfer learning can offer with cancer staging and metastasis detection. Additionally, the model utilizing transfer learning will be trained with a smaller subset of the dataset to determine if using transfer learning reduced the need for a large dataset to get improved results.
Software architecture diagram
Links to project components
Link to complete paper:
Link to software on gitlab: https://code.cs.earlham.edu/afarah18/senior-cs-capstone
Link to video on youtube: https://www.youtube.com/watch?v=G01y0ZLKST4
Link to copy of poster: