# An Efficient Deep Learning Approach for Colon Cancer Detection
## 1 Introduction  
Adenocarcinomas, which damage the lining of the large intestine (colon) and rectum, account for nearly all malignancies of the large intestine and rectum malignancies. Colorectal cancer typically originates as a button-like growth termed a polyp on the surface of the intestinal lining or rectum [1]. The intestine or rectum division, may invade nearby or adjacent lymph nodes. Due to the fact that blood flows from the intestine's wall and a substantial portion of the rectum to the liver, colorectal cancer can metastasize to the liver after spreading to adjacent lymph nodes [2]. Cancer of the large intestine and rectum is the most frequent cancer in Western countries and the second most significant cause of cancer death. When a person is between 40 and 50 years, their risk of acquiring colorectal cancer significantly increases. Around 140,250 persons are affected by colon and rectal cancer. Each year, approximately 50,630 people die in the United States [3]. Colorectal cancer risk is increased by family history and specific dietary factors (low fiber and high fat) [4]. Typical symptoms include blood in feces, fatigue, and weakness. As a result, cancer that is caught early is more curable. In this paper, we propose a novel and efficient system for the automatic early detection of colon cancer from the analysis of histopathological images. Recently, several computer-assisted diagnosis systems (CADs) were introduced to automatically check for signs of tumor or cancer growth in the colon [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Thanks to artificial intelligence (AI) for helping the diagnostic systems accurately detect cancer, the common AI approaches for cancer detection are: (1) detection of colon cancer using machine learning approaches from the analysis of histopathological images [13; 14; 15] and (2) detection of colon cancer using deep learning approaches from the analysis of histopathological images [5; 6; 7; 8; 9; 10; 11; 12]. In machine learning approaches, researchers used stages called preprocessing, feature extraction, feature selection, and optimization and finally classification stage. The preprocessing stage is used to remove any noise that effect on the accuracy of the original images. In the feature extraction stage, all unique features are extracted from the preprocessed image. After that, the most important features are selected from the extracted features in the feature selection stage. The final decision is made using a separate classifier in the classification stage. The recent works in this field using machine learning obtained a good performance for colon cancer detection. However, the cancer detection systems based on machine learning need manual detection of the features and separate classifiers for the detection, making the system more complex and time-consuming when using big data [16]. In addition, most of these methods suffer from overfitting problems and obtain low accuracies when working on big data. Finally, these methods depend on the extracted features and the quality of the feature extraction methods which affect the final classification results. On the other hand, deep learning approaches alleviate the majority of the drawbacks associated with prior machine learning approaches [17]. In these approaches, the feature extraction and classification stages are combined in one stage using the deep model. The researchers employed the systems-based deep learning approaches usually used pretrained models and transfer learning techniques or by establishing their own deep model. The most common deep learning model is the convolutional neural network (CNN), which attained high accuracy for colon cancer detection. Several deep learning applications for medical image analysis, such as detection in 3D ultrasound images, COVID detection, and several types of cancer detection have been implemented in many works of literature [18; 19; 20]. These cancer detection systems based on deep learning often require big data.