AN ADVANCED DEEP LEARNING AND COMPUTER VISION BASED SYSTEM FOR LIVER TUMOR DETECTION AND LOCALIZATION WITH MACHINE LEARNING

Abstract

The proposed system focuses on developing an intelligent medical imaging and predictive platform for liver tumor detection localization and disease analysis using advanced computational techniques. The framework integrates deep learning computer vision and machine learning within a unified web-based environment to enhance diagnostic efficiency. The system processes liver images through convolutional neural networks where tumor presence is identified and spatial localization is achieved for clinical interpretability. Within the evolving landscape of medical image analysis the system is conceptualized as an intelligent framework that enables automated detection while improving decision support accuracy.

Further analysis involves CT scan-based evaluation where tumor size estimation is performed to understand severity and progression. The system also incorporates a machine learning module that predicts liver related diseases using patient clinical parameters thus enabling early-stage risk identification. Against the backdrop of increasing healthcare challenges the system introduces a data driven paradigm that transforms raw inputs into actionable insights. Additionally, a generative AI based doctor assistance module provides interactive guidance enhancing user engagement and accessibility.

By integrating multiple computational layers the platform establishes a scalable and efficient solution contributing to modern healthcare systems and supporting improved clinical outcomes.