Colonoscopy, which refers to the endoscopic examination of colon using a camera, is considered as the most effective method for diagnosis of colorectal cancer. Colonoscopy is performed by a medical doctor who visually inspects one’s colon to find protruding or cancerous polyps. In some situations, these polyps are difficult to find by the human eye, which may lead to a misdiagnosis. In recent years, deep learning has revolutionized the field of computer vision due to its exemplary performance. This study proposes a Convolutional Neural Network (CNN) architecture for classifying colonoscopy images as normal, adenomatous polyps, and adenocarcinoma. The main objective of this study is to aid medical practitioners in the correct diagnosis of colorectal cancer. Our proposed CNN architecture consists of 43 convolutional layers and one fully-connected layer. We trained and evaluated our proposed network architecture on the colonoscopy image dataset with 410 test subjects provided by Gachon University Hospital. Our experimental results showed an accuracy of 94.39% over 410 test subjects.
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机译：结肠镜检查是指使用相机的结肠内窥镜检查的内窥镜检查，被认为是诊断结肠直肠癌的最有效方法。结肠镜检查由一名医生进行，医生在视觉上检查一个人的冒号以找到突出或癌症息肉。在某些情况下，人眼难以找到这些息肉，这可能导致误诊。近年来，由于其示范性表现，深入学习已经彻底改变了计算机视野领域。本研究提出了一种卷积神经网络（CNN）架构，用于将结肠镜检查图像分类为正常，腺瘤息肉和腺癌。本研究的主要目标是帮助医学从业人员在正确的结肠直肠癌诊断中。我们提出的CNN架构由43个卷积层和一个完全连接的层组成。我们培训并在结肠镜检查图像数据集上进行了培训并评估了具有由Gachon University Hospital提供的410个测试对象的Colonoscopy图像数据集。我们的实验结果表明，410个测试对象的准确性为94.39％。
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