论文标题
用于诊断结肠和肺癌组织病理学图像的卷积神经网络
Convolution Neural Networks for diagnosing colon and lung cancer histopathological images
论文作者
论文摘要
肺癌和结肠癌是成人死亡率和发病率的主要原因之一。组织病理学诊断是识别癌症类型的关键组成部分之一。本研究的目的是提出一种计算机辅助诊断系统，用于使用卷积神经网络来诊断肺的鳞状细胞癌和肺癌以及结肠的腺癌，通过评估这些癌症的数字病理图像，以评估卷积神经网络。因此，在不久的将来将人工智能作为有用的技术。总共从LC25000数据集中获取了2500张数字图像，这些数据集包含5000套图像。使用了一种浅的神经网络结构，将组织病理学片段分类为肺癌，腺癌和良性良性的鳞状细胞癌。类似的模型被用于对结肠的腺癌和良性分类。分别记录了肺和结肠的诊断准确性超过97％和96％。
Lung and Colon cancer are one of the leading causes of mortality and morbidity in adults. Histopathological diagnosis is one of the key components to discern cancer type. The aim of the present research is to propose a computer aided diagnosis system for diagnosing squamous cell carcinomas and adenocarcinomas of lung as well as adenocarcinomas of colon using convolutional neural networks by evaluating the digital pathology images for these cancers. Hereby, rendering artificial intelligence as useful technology in the near future. A total of 2500 digital images were acquired from LC25000 dataset containing 5000 images for each class. A shallow neural network architecture was used classify the histopathological slides into squamous cell carcinomas, adenocarcinomas and benign for the lung. Similar model was used to classify adenocarcinomas and benign for colon. The diagnostic accuracy of more than 97% and 96% was recorded for lung and colon respectively.
