# Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
## 2 Simple Image-Processing-Based and Machine-Learning-Based CAD Approaches  
Owing to synthetic approaches based on image processing techniques, conventional CAD systems have been used in diagnosing CRC for a few decades. Table 1 lists a brief overview of the conventional studies regarding simple image processing techniques and ML-based CAD systems that have been researched for use in diagnosing CRC. CAD systems based on simple image processing methods rely on mathematical models explicitly defined by human rules for processing images from one modality to another, and they require the case-by-case tuning of model parameters for optimal performance. Specifically, diagnoses are mainly based on feature engineering methods where feature extraction is carried out either from vessel structural or textural analysis of the image patches using a local binary pattern (LBP) [13]. Solely relying on simple image processing algorithms, these CAD systems [6; 7; 8] can classify tumor regions or identify various traits of malignant tissues in CRC. Although these systems have been a part of digital pathology for the clinical diagnosis of CRC, they are application-specific and are considered heuristic approaches that require strong domain expertise, which depend upon the unique characteristics of the imaging type involved. With the technological advancements in the field of artificial intelligence (AI), a computer can mimic cognitive functions to solve real-world problems by learning all by itself. Within AI, an ML technique that allows computers to learn from real-world data without being explicitly programmed has been extensively applied to the medical imaging domain. Particularly for the clinical diagnosis of CRC patients, several research works based on ML approaches [9; 10; 11; 12] have been conducted. The ML-based techniques utilize the handcrafted feature extraction of predefined morphological features that rely on the shape, color, and textural information of the image data. These types of features are usually extracted using different types of procedures, such as LBP, wavelet transform, gray-level co-occurrence matrices, scale invariant feature transform (SIFT), histogram of oriented gradients (HoG), etc. The extracted features are then subjected to ML-based classification algorithms that include, but are not limited to, the support vector machine, k-nearest  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Technology** & **Publication (Year)** & **Objective** & **Methods Used** & **Image Types** & **Maximum Results** \\ \hline \multirow{8}{*}{Based on image processing techniques} & \multirow{4}{*}{[6] (2011)} & To develop a CAD system & ROI selection, blood vessel & \multirow{2}{*}{Endoscopic extraction and classification.} & Sensitivity = 95.0\% \\  & & for the classification of & & segmentation, and feature extraction and classification. & & Accuracy = 86.8\% \\  & & & & & & Specificity = 87.8\% \\ \cline{2-5}  & & \multirow{4}{*}{[7] (2015)} & To provide the fully & Image acquisition, adaptive & \multirow{4}{*}{Endoscopic extraction = 82.8\%} \\  & & automated and instant & thresholding, nucleus-like spot labeling, removal of artifacts, and feature extraction. & & & Sensitivity = 92.0\% \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline \multirow{8}{*}{Based on image processing techniques} & \multirow{4}{*}{[8] (2017)} & To evaluate the & ROI extraction, grayscale & \multirow{4}{*}{Endoscopic extraction = 82.8\%} \\  & & effectiveness of software & conversion, binarization & & \\  & & developed for endoscopic diagnosis of colorectal & using moving average methods, morphological & & \multirow{4}{*}{Endoscopic images} & Sensitivity = 83.9\% \\  & & & & & Accuracy = 82.8\% \\  & & lesions using magnifying & operations, and feature extraction. & & & Specificity = 82.6\% \\  & & & &neighbors, logistic regression, decision trees, m-Gaussian mixture models, etc.