Automated Approaches For Detecting And Classifying Colon Cancer: A Comprehensive Study

Colon cancer is an important global health issue, which is ranked as the third most frequently diagnosed cancer and the second most prevalent reason for cancer mortality. The early recognition and accurate classification of colon cancer are critical for enhancing the outcomes of patients and treatment strategies. This research investigates the automated detection and classification techniques for colon cancer utilizing artificial intelligence, particularly deep learning and machine learning algorithms. It aims to assess the efficacy of these advanced methodologies in analyzing medical images, such as colonoscopy and histopathology, for the identification of cancerous tissues. The study also addresses some challenges, such as optimizing these models for real-world use, avoiding overfitting, and ensuring they function well across different patient groups. By reviewing the latest developments in this area, this research emphasizes the importance of using automated systems in everyday medical practice to improve the early diagnosis of colon cancer, resulting in better care and patient outcomes.