# Colorectal Cancer Detection Tool Developed with Neural Networks
## 4 Discussion  
Firstly, a discussion of the ANN model results is warranted. A thorough examination of the available literature reveals that there are three training algorithms that have been analyzed. The L-M algorithm has been shown to yield optimal results, demonstrating both superior performance and expedited training times. It is regarded as the superior and most versatile training algorithm, having been employed in numerous models from diverse fields. In determining the most suitable architectural design, two fundamental factors must be taken into consideration--simplicity and performance. In the domain of  
Figure 17: GUI CNN panel. mathematical modeling, the quality of a model is contingent upon the number of hidden layers and neurons employed. It is imperative to note that the efficacy of the model is subject to limitations, which are determined by these parameters. Following a series of empirical trials, it was determined that the optimal model exhibited an MSE of 7.38 for two hidden layers comprising 35 neurons. The system's simplicity is attributable to its feedforward structure, which is widely regarded as one of the most elementary structures. A comparison of the results with those obtained from models derived from literary sources reveals no statistically significant differences. In the study [7], many CRC detection models are discussed. The best ANN-based model has 93% accuracy and uses SELDI for proteomic analysis, but it is different from the data in this paper. The most similar approach had 74% accuracy and used the TNM staging system. However, the model has its limitations. Due to using expert-derived scores, the obtained model may have limited generalizability and objectivity. The CNN model is chosen based on multiple factors. It could be the model with the smallest resolution, given its small training time and low computation requirements. The accuracy is in the 90% range, but using a small resolution can lead to information loss. The GUI rescales the input image to a similar resolution as the training image, reducing the loss of information. A medium resolution is selected to minimize information loss and computational demands. The accuracy is comparable to results from other studies. In the study [7], several CNN-based models are presented with a wide range of values for accuracy. Simple structures have values from 75% to 90%. Complex structures reach 99%. This study balances complexity and performance and explores additional diagnoses with the output of ulcers. Adding labels and images reduces accuracy, showing that many labels for diseases could harm a CNN model. The GUI uses the models to make the tool user-friendly for medical use. Many studies compare neural network-based models, but few use multiple models for diagnosis. This study's contribution is a more complex tool for thorough diagnosis using both types of input variables. Nevertheless, the data originate from two distinct sources that are not interconnected: the Kvasir dataset, utilized for the CNN model, and an augmented novel dataset, employed for the ANN model. It has been demonstrated that the two datasets can be utilized to train adequate models with their respective datasets. However, it cannot be concluded that better performance could be obtained by using a combination of both models for diagnosis. In the context of future developments, the potential exists for the creation of a CNN-based tool that would have the capability of coloring affected zones of CRC or of establishing a bounding box around these zones with the objective of highlighting them. Firstly, the utilization of this technology has the potential to reduce the time required for medical personnel to complete a diagnosis. Furthermore, it may serve to illuminate areas that might not otherwise be given full consideration. Secondly, the primary objective of these instruments is to mitigate the likelihood of misdiagnosis. Finally, the visual nature of the image is more appealing than that of a text message.