2017/11/29 by Marcia Hon, Naimul Khan, Hon, Marcia +1
Computer Science · Engineering · Neuroscience · #AI in cancer detection #Brain Tumor Detection and Classification #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Imaging and Analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.1711.11117
Presented at IEEE BIBM 2017
arxiv created 2017/11/29 · openalex publication_date 2017/11/29 · arxiv updated 2017/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years. Recent success of deep learning in computer vision have progressed such research further. However, common limitations with such algorithms are reliance on a large number of training images, and requirement of careful optimization of the architecture of deep networks. In this paper, we attempt solving these issues with transfer learning, where state-of-the-art architectures such as VGG and Inception are initialized with pre-trained weights from large benchmark datasets consisting of natural images, and the fully-connected layer is re-trained with only a small number of MRI images. We employ image entropy to select the most informative slices for training. Through experimentation on the OASIS MRI dataset, we show that with training size almost 10 times smaller than the state-of-the-art, we reach comparable or even better performance than current deep-learning based methods.