Neurocancel-X: A Low-Cost Early-Stage Cancer Detection And Prevention System And Method Thereof For Mankind

Research Scholar: Dr. Soumya Ranjan Jena, B.Tech, M.Tech, PhD

Post-Doctoral Fellowship Research Certificate Program Research Scholar

Under Ramnath Prasad Institute of Higher Education Foundation, East Champaran

Funding by CSR Agency

Background: Early-stage cancer detection remains one of the most effective strategies for reducing cancer-related mortality. However, conventional diagnostic techniques such as imaging, histopathology, and molecular testing often require specialized infrastructure, trained personnel, and substantial financial resources, limiting accessibility in low-resource settings. There is a growing need for affordable, rapid, and intelligent diagnostic systems capable of supporting timely clinical decision-making.

Objective: Neurocancel-X is proposed as a low-cost, artificial intelligence-enabled early-stage cancer detection and prevention system designed to improve screening accessibility, diagnostic accuracy, and preventive healthcare. The system aims to integrate multimodal patient information, including clinical history, laboratory biomarkers, medical imaging, and lifestyle risk factors, into an intelligent decision-support framework.

Methods: The proposed method utilizes machine learning and deep learning algorithms for automated feature extraction, risk stratification, and cancer probability prediction. A neuromorphic-inspired computational architecture is incorporated to optimize energy efficiency and real-time processing. The framework is intended to support explainable AI techniques, allowing clinicians to interpret prediction outcomes while maintaining transparency. Preventive recommendations are generated based on personalized risk profiles and evidence-based clinical guidelines.

Expected Outcomes: Neurocancel-X is expected to provide rapid, accurate, and cost-effective screening for multiple cancer types, facilitating early diagnosis, reducing unnecessary invasive investigations, and improving patient outcomes. Its scalable architecture makes it suitable for deployment in hospitals, primary healthcare centers, mobile diagnostic units, and telemedicine platforms, particularly in underserved regions.

Conclusion: Neurocancel-X represents an innovative concept that combines artificial intelligence, neuromorphic computing, and preventive oncology into an integrated decision-support platform. If clinically validated, the proposed system has the potential to enhance population-level cancer screening, promote early intervention, and contribute to more accessible, affordable, and equitable cancer care worldwide.

Keywords: Artificial Intelligence, Early Cancer Detection, Neuromorphic Computing, Clinical Decision Support System, Machine Learning, Preventive Oncology, Digital Health, Low-Cost Healthcare Technology.