Paper Title
Leveraging Deep Learning and Transfer Learning for Enhanced Colon Cancer Detection: Empowering Early Diagnosis Through Image Analysis

Abstract
Our study addresses the global concern of colon cancer by leveraging deep learning, specifically the ResNet architecture, to enhance early detection. Using a dataset of colonoscopy and histopathology images, our ResNet-based model demonstrates exceptional performance, achieving over 99% accuracy and a precision of 0.995 for adenocarcinoma cases. This underscores its efficacy in distinguishing cancerous lesions while minimizing false positives. The clinical relevance lies in the model's potential for early colon cancer detection, serving as a valuable tool for healthcare professionals. Its robustness and ability to generalize to unseen data position it as a promising asset in clinical decision-making, emphasizing the importance of timely intervention. Acknowledging ethical considerations and limitations, our research advocates for the responsible deployment of deep learning models in healthcare. Future directions include further validation, integration into clinical workflows, and exploration of diverse datasets for adaptability. Our study significantly contributes to medical image analysis, offering hope in the fight against colon cancer, with implications extending beyond machine learning to positively impact patient care and reshape early cancer detection practices. Keywords - Colon Cancer, Resnet, Deep Learning, Early Detection, Medical Image Analysis, Classification.