Colon cancer is a common type of carcinoma that occurs in the large intestine. This type of cancer affects millions of people around the world each year. Early and accurate diagnosis is very important in the treatment of colon cancer as in other types of cancer. Thanks to early and accurate diagnosis, many people can get rid of this disease with less damage. Medical imaging techniques are widely used in the early diagnosis, follow-up, and after the treatment process of colon cancer. Therefore, manually controlling a large number of medical images and their interpretation is a difficult process and consumes more time. In addition, the interpretation of data with traditional methods in this process can cause misdiagnosis due to human errors. For this reason, computer-aided systems can be used in the diagnosis of colon cancer in order to both help experts and carry out the process more quickly and successfully. In this study, a novel method named by us, CNN-based, MA_ColonNET is developed for detecting colon cancer image data. A 45-layer model in MA_ColonNET has been used to classify. A success (accuracy) rate of 99.75% has been achieved by means of the new model. It is shown that the proposed model can detect colon cancer earlier. In this way, the treatment process can be carried out more successfully.

中文翻译：

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使用新的CNN模型对结肠组织病理学图像的结肠腺癌和结肠良性组织进行分类：MA_ColonNET

结肠癌是发生在大肠中的常见癌症类型。这种癌症每年影响全世界数百万人。与其他类型的癌症一样，早期和准确的诊断对于结肠癌的治疗非常重要。由于早期和准确的诊断，许多人可以以更少的伤害摆脱这种疾病。医学影像技术广泛应用于结肠癌的早期诊断、随访和治疗过程。因此，手动控制大量的医学图像及其解释是一个困难的过程，并且消耗更多的时间。此外，在这个过程中用传统方法对数据的解释可能会由于人为错误而导致误诊。为此原因，计算机辅助系统可用于结肠癌的诊断，以帮助专家并更快、更成功地执行该过程。在这项研究中，我们开发了一种新的方法，即基于 CNN 的 MA_ColonNET，用于检测结肠癌图像数据。MA_ColonNET 中的 45 层模型已用于分类。新模型的成功率（准确率）达到了99.75%。结果表明，所提出的模型可以更早地检测出结肠癌。这样，处理过程可以更成功地进行。75% 已通过新模型实现。结果表明，所提出的模型可以更早地检测出结肠癌。这样，处理过程可以更成功地进行。75% 已通过新模型实现。结果表明，所提出的模型可以更早地检测出结肠癌。这样，处理过程可以更成功地进行。