2018 JRNL PP Jianpeng Zhang - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
The classification of medical images and illustrations
from the biomedical literature is important for automated
literature review, retrieval, andmining. Although deep
learning is effective for large-scale image classification, it
may not be the optimal choice for this task as there is only
a small training dataset. We propose a combined deep and
handcrafted visual feature (CDHVF) based algorithm that
uses features learned by three fine-tuned and pretrained
deep convolutional neural networks (DCNNs) and two handcrafted
descriptors in a joint approach. We evaluated the
CDHVF algorithm on the ImageCLEF 2016 Subfigure Classification
dataset and it achieved an accuracy of 85.47%,
which is higher than the best performance of other purely
visual approaches listed in the challenge leaderboard. Our
results indicate that handcrafted features complement the
image representation learned by DCNNs on small training
datasets and improve accuracy in certain medical image
classification problems.