期刊
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
卷 64, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2019.102609
关键词
Image classification; CNN; Quality score
资金
- General Projects in the Field of Social Development of Shaanxi Science and Technology Department), Shaanxi University of Traditional Chinese Medicine Innovation Team of Fuzheng Guzhong Traditional Chinese Medicine for the Treatment of Malignant Tumors [2017SF-306]
Image classification plays an important role in computer vision and its applications, such as scene categorization, image retrieval. Convolutional neural network based methods have shown competitive performance in image classification, which aims to exploit deep feature of training images. In this paper, based on CNN methods and image quality assessment (IQA) algorithms, we propose a novel method for medical application, that is breast cancer classification. First, we leverage CNN architecture to calculate the number of pixels in the lesions, where maximum pooling layers are used. Then, large density of pixel regions will be assigned with large quality scores, which reflect more texture and grayscale features. Finally, we construct a multi-SVM based image kernel using obtained quality scores to achieve breast cancer classification. Experimental results show our proposed method outperforms single recognition based image classification methods such as pixel grayscale or gradient. (C) 2019 Elsevier Inc. All rights reserved.
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