Efficient Colon Cancer Detection Using Transfer Learning with EfficientNet: A Deep Learning Approach for Enhanced Diagnostic Accuracy

Early detection is critical because colon cancer is a leading cause of cancer deaths worldwide. Traditional diagnostic techniques are invasive, expensive, and often inaccessible. This study leverages a state-of-the-art deep learning architecture, EfficientNetB3, to build an efficient and accurate framework for colon cancer detection using histopathological images. By utilizing transfer learning and advanced preprocessing techniques, the model achieves an excellent accuracy of 98.86% with high precision and recall, minimizing false positives and false negatives. The model's robustness and generalization capability are further enhanced by data augmentation and optimization methods, such as Adamax and batch normalization. This research demonstrates that integrating EfficientNet with a scalable and lightweight design performs well in adenocarcinoma detection from normal tissues, thereby holding the promise to serve as a non-invasive and reliable diagnostic tool. Ultimately, deep learning holds great promise for early cancer detection, providing a solid foundation for future developments of AI-driven healthcare solutions.