# Adenocarcinoma Recognition in Endoscopy Images Using Optimized Convolutional Neural Networks
## 1 Introduction  
The Gastrointestinal (GI) tract, or the digestive tract, is a system of organs responsible for digestion in humans. Like all other parts of the human body, the GI tract may have various diseases such as inflammatory diseases, autoimmune diseases, tumorous diseases, etc. To diagnose these diseases, clinical medical examination is required, which may include procedures like fecal occult blood test, endoscopy, etc. In general, colonoscopy is considered as the most accurate method for identifying lesions in one's colon. In addition, colonoscopy is also used for therapeutic purposes. Colorectal cancer is the second leading cause of cancer deaths and is one of the five most important cancers to be screened. According to the National Statistical Office (NSO) survey in 2016, colorectal cancer was the third most common cause of lung cancer and liver cancer. In the past, the colorectal cancer incidence rate was high in Western countries. However, the incidence rate is rapidly increasing in South Korea as well. According to the adenoma-cancer continuum hypothesis, 95% of colorectal cancers occurring in the general population are advanced through the adenoma stage. Therefore, early detection and elimination of polyps during the adenoma stage are crucial in the prevention of colorectal cancer [1]. Colorectal cancer is diagnosed by a physician, and the authenticity of this diagnosis depends on the physician's experience. Although certain objective criteria are used to ensure an accurate diagnosis, however, most physicians tend to follow a subjective criterion. Therefore, the results of an endoscopic colonoscopy have a very subjective disadvantage [2]. The introduction of a system based on artificial intelligence can assist in obtaining a more accurate diagnosis and reduce human errors. Attempts to analyze medical images using computerized methods date back many decades. The concept of Computer Aided Diagnosis (CAD) appeared in the 1970s when scanned medical images were analyzed on a computer. In the 1970s and 1990s, rule-based systems and expert systems were widely used. Rule-based systems use low-level image processing to extract edges and lines using filters. Mathematical structures are used to match circles and ellipses to obtain and analyze components. On the other hand, an expert system is evaluated as Good Old-Fashioned Artificial Intelligence (GOFAI) by analyzing the results of images using several conditional statements (if-else statements) [3]. Training data to improve the performance of a system were popularized in the late 1990s. This process required two main steps, namely feature extraction and classification. Features such as color, shape, texture, etc., are extracted during the feature extraction step. The most crucial step is to extract important features to represent the image. The extracted features are analyzed using various machine learning algorithms. The work done by [3; 4; 5; 6; 7] followed the same approach, which included feature extraction steps having several linear classifiers for classification. However, such methods rely on texture analysis, which requires expert knowledge about the features during the extraction process. Hence, they lack generalization and cannot be useful for transfer capabilities. Currently, deep learning is widely used in medical image analysis. The 1998 paper by [PERSON] [8] laid the foundation of today's deep learning. Artificial intelligence using deep learning has shown excellent results in various fields such as speech recognition, language discrimination, behavior recognition, and image retrieval. Mostly medical image analysis deals with diagnosing diseases and detecting the affected area. Disease diagnosis using artificial intelligence is an active research area due to the development and state-of-the-art performance of deep learning. Recently, CNN has been reported to be highly useful in the field of endoscopy, especially Esophagogastroudoendoscopy (EGD), capsule endoscopy, and colonoscopy.