A comparative study on polyp classification using convolutional neural networks
A comparative study on polyp classification using convolutional neural networks
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DOI：
10.1371/journal.pone.0236452
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发表时间：
2020-07-30
期刊：
影响因子：
3.7
通讯作者：
Wang, Guanghui
中科院分区：
文献类型：
--
作者：
Patel, Krushi;Li, Kaidong;Wang, Guanghui
结直肠癌是美国男性和女性中诊断出的第三大常见癌症。大多数结肠直肠癌开始于结肠或直肠的内层生长，称为“息肉”。并非所有的息肉都是癌性的，但有些会发展成癌症。早期发现和识别息肉的类型对于预防癌症和改变结果至关重要。然而，由于内窥镜检查的不同照明条件、不同的纹理、外观和息肉之间的重叠形态，息肉的视觉分类是具有挑战性的。更重要的是，胃肠病学家对息肉模式的评价是主观的，导致观察者之间的一致性较差。深度卷积神经网络在各种对象类别的对象分类方面非常成功。在这项工作中，我们比较了息肉分类的最先进的一般对象分类模型的性能。我们使用由两种类型的息肉组成的157个视频序列的数据集训练了总共6个CNN模型：增生性和腺瘤性。我们的研究结果表明，最先进的CNN模型可以成功地对息肉进行分类，其准确性与胃肠病学家报道的相当或更好。本研究的结果可以指导未来息肉分类的研究。
Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. Most colorectal cancers start as a growth on the inner lining of the colon or rectum, called 'polyp'. Not all polyps are cancerous, but some can develop into cancer. Early detection and recognition of the type of polyps is critical to prevent cancer and change outcomes. However, visual classification of polyps is challenging due to varying illumination conditions of endoscopy, variant texture, appearance, and overlapping morphology between polyps. More importantly, evaluation of polyp patterns by gastroenterologists is subjective leading to a poor agreement among observers. Deep convolutional neural networks have proven very successful in object classification across various object categories. In this work, we compare the performance of the state-of-the-art general object classification models for polyp classification. We trained a total of six CNN models end-to-end using a dataset of 157 video sequences composed of two types of polyps: hyperplastic and adenomatous. Our results demonstrate that the state-of-the-art CNN models can successfully classify polyps with an accuracy comparable or better than reported among gastroenterologists. The results of this study can guide future research in polyp classification.