Data enhancement to improve CNN accuracy for early detection of colorectal cancer

Multifactorial colorectal cancer is a frequently diagnosed disease that is a leading cause of cancer-related mortality worldwide. Optimizing the patient's chances of recovery requires early detection. This research aims to implement a Convolutional Neural Network (CNN) model that can classify colon endoscopic images into three categories: normal, polyp, and cancer, using the VGG-16 architecture. This study employed the following techniques: K-means clustering to manage outliers, data augmentation to enhance the diversity of datasets, and Pearson correlation analysis to confirm the relationship between the augmentation and initial datasets. Data processing steps resulted in an enhanced dataset. Compared to models trained on the initial dataset, the results demonstrated a significant increase in accuracy and generalizability for CNN models trained on the enhanced dataset, which included outlier handling, augmentation, validation, and class balancing. The model's performance evaluation showed an accuracy of 86%, with notable improvements in F1-score and recall for the cancer class. These findings indicate that the model better-classified images after dataset enhancement. H ighlight • Data enhancement sequences improve the accuracy of CNN for colorectal cancer detection. • Outlier handling, augmentation, validation, and class balancing improve data quality. • Pearson's correlation ensures the validation and diversity of augmented medical images. • Enhanced dataset substantially improves CNN generalizability and cancer recall. • The framework improves the generalizability and reliability in medical image analysis