Akbari Mojtaba, Mohrekesh Majid, Rafiei Shima, Reza Soroushmehr S M, Karimi Nader, Samavi Shadrokh, Najarian Kayvan
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:65-68. doi: 10.1109/EMBC.2018.8512226.
Colorectal cancer is one of the common cancers in the United States. Polyps are one of the major causes of colonic cancer, and early detection of polyps will increase the chance of cancer treatments. In this paper, we propose a novel classification of informative frames based on a convolutional neural network with binarized weights. The proposed CNN is trained with colonoscopy frames along with the labels of the frames as input data. We also used binarized weights and kernels to reduce the size of CNN and make it suitable for implementation in medical hardware. We evaluate our proposed method using Asu Mayo Test clinic database, which contains colonoscopy videos of different patients. Our proposed method reaches a dice score of 71.20% and accuracy of more than 90% using the mentioned dataset.
结直肠癌是美国常见的癌症之一。息肉是结肠癌的主要病因之一，早期发现息肉将增加癌症治疗的机会。在本文中，我们提出了一种基于具有二值化权重的卷积神经网络的信息帧新型分类方法。所提出的卷积神经网络以结肠镜检查帧以及帧的标签作为输入数据进行训练。我们还使用二值化权重和内核来减小卷积神经网络的规模，使其适合在医疗硬件中实现。我们使用包含不同患者结肠镜检查视频的阿苏梅奥测试诊所数据库对我们提出的方法进行评估。使用上述数据集，我们提出的方法的骰子系数达到71.20%，准确率超过90%。
