A UNIFIED CNN-BASED FRAMEWORK FOR GENERALIZED AND REAL-TIME COLORECTAL CANCER PREDICTION AND DIAGNOSIS: BRIDGING DATA GAPS, ENHANCING INTERPRETABILITY, AND PERSONALIZING OUTCOMES

Colorectal cancer (CRC) remains one of the most common and deadly cancers worldwide, making early detection and accurate diagnosis more important than ever. In this work, we introduce UniCRC-Net—a smart, CNN-based system designed to predict and diagnose colorectal cancer in real time, using structured patient data. Unlike many current machine learning and deep learning models, which struggle with scattered data, lack of explainability, and generic predictions, this unified approach brings together multiple patient details—like age, gender, pathology scores, gene markers, diet, and environment—into a streamlined and intelligent framework. The model is trained on a carefully constructed synthetic dataset and optimized using the Adam algorithm over 50 training epochs. It performs exceptionally well, hitting a perfect 100% accuracy, F1-score, and AUC, which means it’s both highly precise and consistent in identifying cancer cases. The results are backed by clear visualizations—such as accuracy and loss graphs, a confusion matrix, and a sharp ROC curve—demonstrating how stable and dependable the model is throughout its training. What sets UniCRC-Net apart is its real-time capability, its ability to personalize predictions, and its transparent design, which makes it easier to trust in clinical use. It's also built with the future in mind—ready for integration with federated learning systems that protect patient privacy while enabling collaboration across hospitals and regions. In short, this framework not only fills major gaps in CRC diagnostics but also moves us a step closer to AI-powered, patient- specific cancer care that’s fast, secure, and clinically meaningful.