基于迁移学习的腹部CT图像肾肿瘤识别

Sefatul Wasi, S. Alam, Rashedur M. Rahman, M. A. Amin, Syoji Kobashi
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引用次数: 0

摘要

肾肿瘤是一种影响肾细胞的健康问题,根据其类型可能导致死亡。良性肿瘤是没有问题的,而恶性肿瘤则有肾癌的危险。通过基于深度学习技术的肾肿瘤识别,早期发现和诊断成为可能。本文提出了一种基于迁移学习的基于深度卷积神经网络(DCNN)的肾肿瘤CT图像识别方法。对5284张图像进行了评价。最终的准确率、精密度、召回率、特异性和F1评分分别为92.54%、80.45%、93.02%、92.38%和0.8628。
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Kidney Tumor Recognition from Abdominal CT Images using Transfer Learning
Kidney tumor is a health concern that affects kidney cells and may leads to mortality depending on their type. Benign tumors can be unproblematic whereas malignant tumors pose the threat of kidney cancer. Early detection and diagnosis are possible through kidney tumor recognition based on deep learning techniques. In this paper, a method based on transfer learning using deep convolutional neural network (DCNN) is proposed to recognize kidney tumor from computed tomography (CT) images. The proposed method was evaluated on 5284 images. The final accuracy, precision, recall, specificity and F1 score were 92.54%, 80.45%, 93.02%, 92.38% and 0.8628, respectively.
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