# Colorectal Cancer Detection Tool Developed with Neural Networks
## 1 Introduction  
Over the course of a decade, an increasing number of studies on cancer detection have been published, a development largely driven by the high mortality rate of the disease [1]. The present study is focused on colorectal cancer. According to the most recent statistics,this type of cancer has the second-highest mortality rate, with lung cancer ranking at the top [2]. The danger of CRC lies in the ambiguous symptoms, which can easily be mistaken for other less severe diseases [3], and the possibility to misdiagnose the patient. Although early diagnosis remains the most important determinant of survival in CRC, limitations and obstacles have been reported. Research has demonstrated that colonoscopies are associated with a reduced probability of survival. This outcome is attributable, at least in part, to the necessity for image interpretation by the examining physician, which can lead to an overreliance on the right colon location during the diagnostic procedure [4]. In addition, a meta-analysis by [PERSON] et al. recently reported a 21% prevalence of polyps [5]. Early and precise detection are therefore required to assist the diagnosis of CRC patients and increase their survival [6]. In the review [7], different approaches for cancer detection are studied. In the context of CRC, a wide array of CNN-based methodologies exists, incorporating or not incorporating additional techniques to enhance performance. In the case of the studies [8; 9; 10; 11; 12], simpler CNN structures are trained with variable accuracy, ranging between 60% and 99.86%. Studies [10; 13; 14; 15; 16; 17] utilized either special architectures or techniques like the wavelet transform or AutoML to enhance the performance of their models. In these cases, the performance was reported to elicit a wider interval, with accuracy ranging from 49% to 96%. Lastly, some researchers use ANN with different types of inputs [18; 19; 20; 21; 22]. In these cases, the performances go from as low as 63% to as high as 93%. This finding demonstrates that a satisfactory model can be trained even with a limited set of data, underscoring the potential for effective machine learning with minimal input. In the aforementioned models, either images are utilized as inputs or biological parameters are derived from blood tests or stool analysis. However, a comprehensive diagnosis necessitates the incorporation of both biologic test results and, when indicated, a colonoscopy. Moreover, the analysis of a broader array of diseases is contingent upon the identification of shared symptoms and established detection methodologies. In the same review, different types of carcinoma and diagnosis tools are studied. For lung cancer, CNN or other neural network-based models are used in conjunction with a performance-enhancing technique. For example, refs. [23; 24] use PNN and have performances of 92\(-\)95%. In the study [25], CNN is used with 97% accuracy. In research [26; 27; 28; 29], ANN-based models are trained. The best results come from [26], where an accuracy of 97.86% is obtained. In this scenario, AdaBoost is employed in conjunction with an ANN and the enose concept is explored. In [30], DITNN is utilized, and an accuracy of 98.4% is obtained. For pancreatic cancer, mostly ANN-based methods are studied. In [29; 31; 32], the performances are between 71% and 85%. The most optimal models are obtained with MNN, a combination of CNN and ANN for the same purpose. The precision of the model is determined to be 95% during the validation process. For the diagnosis of thyroid cancer, several CNN-type methods exhibit a range of performance rates, with accuracies reported between 83% and 99%. The most optimal outcomes are achieved through the utilization of GoogleNet-based CNNs. A substantial corpus of research has demonstrated the efficacy of ANN-based models, with reported accuracy ranging from 83% to 100% [33]. A substantial number of tools created by other researchers have been examined, and it is evident that most of these models employ only a single form of input.