# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 4 Discussion
In order to address the complications created by limited data, other techniques, such as transfer learning, can mitigate the model's dependency on training data sample size by using pretrained weights of other large-scale datasets to initialize the model hyperparameters. 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. Similarly, a clear difference in the accuracy of the DL model can be perceived, depending upon the use of preprocessed and non-preprocessed datasets to train any model. For a quantitative evaluation of the DL model, data preprocessing such as ROI extraction, color normalization, thresholding, etc., must be incorporated. In addition, data cleaning/preprocessing eliminates a portion of low-quality data or outliers, such as image pairs with suboptimal registration. Apart from that, image annotation such as tumor tissue labeling in CRC is considered a highly sophisticated and time-consuming task, and thus, there is a need for highly skilled and experienced pathologists to prepare high-quality datasets for training and testing. Not only limited to image annotations, the requirement for medical associates and pathologists is also extremely important in order to consider the values and preferences of the patients, the medical judgments, the interventional procedures, policy making, and other tasks that cannot be accomplished by computer programs alone. Therefore, the need for pathologists remains essential for medical practices diagnosing not only CRC but also other cancer variants. Current DL models exist in various forms and architectures, and frequent optimizations of those models are being released to ensure highly accurate results in CRC diagnosis. However, only an abundant number of experiments and user-based experiences can guarantee the reliability of those models for clinical purposes. Therefore, it is necessary to apply several DL algorithms to identify and detect each type of CRC malignancy and compare them to find the optimal diagnosis procedure. Moreover, improving the existing theoretical foundation of the DL on the basis of the type of experimental data must be taken into consideration to quantify the performance of multiple DL-based CRC detection modalities. Such improvements must address the data-specific assessment of any algorithm, its computational complexity, and the hyperparameter tuning strategies [93]. The fact that currently incorporated models can be biased towards non-CRC datasets cannot be overlooked, and thus, specific criteria should be validated for CRC-specific DL models in order to obtain intuitive insights into their optimization characteristics and certainties. CRC diagnosis and prognosis with DL technologies are almost ready to be commercialized for practical use cases in clinical settings. By exploring several other opportunities regarding data preparation and model architectures, there is still room for improvement in the accuracy of those models that are still in the suboptimal phase.