Using deep learning to enhance head and neck cancer diagnosis and classification

摘要

Head and neck cancer detection is performed by collecting 26019 CT scan images from Cancer Imaging Archive (TCIA) as this cancer rapidly increases now a days. This paper mainly focuses on classifier Deep learning framework in h2o that gives better accuracy. At first CT scan image of head and neck cancer is given as input to the system and processed through the image processing technique called weiner filter. Then process the image through the segmentation technique called fuzzy c means algorithm. After that feature extraction technique Gray Level Co-Occurrence Matrix (GLCM) is used to extract the features. These features are given to classifier to train the model and finally it obtains the satisfactory results with 98.8% accuracy.