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Cystoscopic image classification by an ensemble of VGG-nets | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 53، دوره 12، شماره 1، مرداد 2021، صفحه 693-700 اصل مقاله (546.46 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.4876 | ||
| نویسنده | ||
| Ehsan Kozegar* | ||
| Faculty of Technology and Engineering (Eastern Guilan), University of Guilan, Guilan, Iran. | ||
| تاریخ دریافت: 30 مهر 1399، تاریخ بازنگری: 15 بهمن 1399، تاریخ پذیرش: 27 بهمن 1399 | ||
| چکیده | ||
| Over the last three decades, artificial intelligence has attracted lots of attentions in medical diagnosis tasks. However, few studies have been presented to assist urologists to diagnose bladder cancer in spite of its high prevalence worldwide. In this paper, a new computer aided diagnosis system is proposed to classify four types of cystoscopic images including malignant masses, benign masses, blood in urine, and normal. The proposed classifier is an ensemble of a well-known type of convolutional neural networks (CNNs) called VGG-Net. To combine the VGG-Nets, bootstrap aggregating approach is used. The proposed ensemble classifier was evaluated on a dataset of 720 images. Based on the experiments, the presented method achieved an accuracy of 63% which outperforms base VGG-Nets and other competing methods. | ||
| کلیدواژهها | ||
| Cystoscopy؛ Classification؛ Deep Learning؛ Bootstrap Aggregating MSC: 68T10 | ||
| مراجع | ||
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