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CLASSIFICATION OF MEDICAL IMAGES IN THE BIOMEDICAL LITERATURE BY JOINTLY USING DEEP AND HANDCRAFTED VISUAL FEATURES

Oleh   Jianpeng Zhang [-]
Kontributor / Dosen Pembimbing : Yong Xia, Yutong Xie, Michael Fulham, and David Dagan Feng
Jenis Koleksi : Jurnal elektronik
Penerbit : Lain-lain
Fakultas :
Subjek :
Kata Kunci : Medical image classification, deep convolutional neural network (DCNN), back-propagation neural network (BPNN), ensemble learning.
Sumber : https://ieeexplore.ieee.org/Xplore/home.jsp
Staf Input/Edit : Irwan Sofiyan  
File : 1 file
Tanggal Input : 2021-04-05 13:18:16

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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.