Abstract
It is very important to make an objective evaluation of colorectal cancer histological images. Current approaches are generally based on the use of different combinations of textual features and classifiers to assess the classification performance, or transfer learning to classify different organizational types. However, since histological images contain multiple tissue types and characteristics, classification is still challenging. In this study, we proposed the best classification methodology based on the selected optimizer and modified the parameters of CNN methods. Then, we used deep learning technology to distinguish between healthy and diseased large intestine tissues. Firstly, we trained a neural network and compared the network architecture optimizers. Secondly, we modified the parameters of the network layer to optimize the superior architecture. Finally, we compared our well-trained deep learning methods on two different histological image open datasets, which comprised 5000 H&E images of colorectal cancer. The other dataset was composed of nine organizational categories of 100,000 images with an external validation of 7180 images. The results showed that the accuracy of the recognition of histopathological images was significantly better than that of existing methods. Therefore, this method is expected to have great potential to assist physicians to make clinical diagnoses and reduce the number of disparate assessments based on the use of artificial intelligence to classify colorectal cancer tissue.
Highlights
Colorectal cancer (CRC) is the third most common form of cancer, accounting for about 10% of all cases in the world [1]
Experimental Results Since deep learning technique will be adopted in this study, the performance of a convolutional neural network (CNN)
Based on the performance of final results, only stochastic gradient descent with momentum (SGDM), root mean square propagation (RMSProp), adaptive moment estimation (Adam) are listed since their performance are overall better than other optimizers for different network models
Summary
Colorectal cancer (CRC) is the third most common form of cancer, accounting for about 10% of all cases in the world [1]. Optical colonoscopy is the medical procedure that is usually used to examine a series of abnormalities on the surface of the colon, including their location, morphology and pathological changes to make a clinical diagnosis. This improves the accuracy of the diagnosis and the ability to predict the severity of the disease in order to apply the most appropriate clinical treatment. Subjective evaluation is generally performed by pathologists who manually review the histological slides images of CRC tissue, which remains the standard for cancer diagnosis and staging. The universal automatic classification of CRC pathological tissue slide images for fair evaluation has an important clinical significance
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