Deep Learning–Driven Early Detection of Colorectal Cancer Using Colonoscopy Imaging

Abstract: Colorectal cancer (CRC) stands among the top three causes of cancer deaths that occur throughout the world because early detection proves vital for increasing patient survival rates. Clinicians require advanced skills to perform traditional colonoscopy because this method remains the accepted standard for diagnosing colorectal conditions yet demonstrates a high risk of human mistakes while searching for tiny or flat polyps. The research introduces ColonVision, an intelligent deep learning system, which uses colonoscopy images for early colorectal cancer detection. The method uses advanced convolutional neural networks (CNNs) to achieve automatic tissue pattern identification and tissue pattern classification from endoscopic images with exceptional accuracy. The study trains and validates the model through publicly accessible medical imaging datasets which test the model's performance after applying image normalization and augmentation and noise reduction preprocessing methods. The model evaluation process uses standard metrics to measure performance through accuracy and precision and recall and F1-score, which shows the model achieved better results than conventional machine learning methods. The framework aims to decrease instances of false negative results which will help doctors make correct assessments during the initial stages of patient diagnosis.

The results demonstrate how deep learning can enhance colorectal cancer screening processes by delivering a diagnostic support system which operates with both high reliability and efficient performance and the ability to scale. The research advances toward implementing artificial intelligence systems in medical imaging, which will enable healthcare professionals to achieve faster patient diagnosis results with decreased chances of making diagnostic mistakes that will lead to better patient care.

Keywords: Colorectal Cancer Detection, Medical Image Analysis, Deep Learning, Colonoscopy Images.