Abstract
Unprecedented breakthroughs in the development of graphical processing systems have led to great potential for deep learning (DL) algorithms in analyzing visual anatomy from high-resolution medical images. Recently, in digital pathology, the use of DL technologies has drawn a substantial amount of attention for use in the effective diagnosis of various cancer types, especially colorectal cancer (CRC), which is regarded as one of the dominant causes of cancer-related deaths worldwide. This review provides an in-depth perspective on recently published research articles on DL-based CRC diagnosis and prognosis. Overall, we provide a retrospective synopsis of simple image-processing-based and machine learning (ML)-based computer-aided diagnosis (CAD) systems, followed by a comprehensive appraisal of use cases with different types of state-of-the-art DL algorithms for detecting malignancies. We first list multiple standardized and publicly available CRC datasets from two imaging types: colonoscopy and histopathology. Secondly, we categorize the studies based on the different types of CRC detected (tumor tissue, microsatellite instability, and polyps), and we assess the data preprocessing steps and the adopted DL architectures before presenting the optimum diagnostic results. CRC diagnosis with DL algorithms is still in the preclinical phase, and therefore, we point out some open issues and provide some insights into the practicability and development of robust diagnostic systems in future health care and oncology.
Highlights
Global cancer statistics from 2018 show that the incidence of colorectal cancer (CRC)ranks highest after lung cancer and breast cancer, and worldwide, it accounts for approximately 10% of the total annual cancer patients among both men and women [1]
We identify and list some of the publicly available imaging datasets collected and archived from various independent sources, which are standardized for deep learning (DL)-based CRC diagnosis
Because real-world medical imaging data are hard to acquire, data augmentation and synthetic imaging techniques can be helpful to enhance the accuracy of DL models in diagnosing CRC
Summary
Global cancer statistics from 2018 show that the incidence of colorectal cancer (CRC)ranks highest after lung cancer and breast cancer, and worldwide, it accounts for approximately 10% of the total annual cancer patients among both men and women [1]. People aged 65 years and above are the most prevalent victims of this disease, the risk in younger patients is significant, with the highest risk due to heredity (35%) followed by other factors such as obesity, bad nutritional habits, and smoking [2] These rates show no trend toward decline, but rather are expected to increase by more than 60% in the decade, with more than two million new diagnoses and over a million deaths by the decade [3]. With routine screening being an important step for the reduction in mortality rates of this disease, colonoscopy (an endoscopic method) is considered a primary and straightforward clinical diagnosis method of choice for CRC [4] Aside from this method, medical imaging techniques such as CT colonography, a complementary imaging method for polyp detection in CRC, and the histological evaluation of hematoxylin and eosin (H&E) slides remain indispensable approaches to subtle inspections for CRC. A more standardized and automated technique based on computer-aided diagnosis (CAD) has gained a lot of interest and demand lately
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