NANO-OPTIMIZED DEEP LEARNING FOR EARLY CANCER DETECTION USING NANOSENSOR DATA

Cancer is still a major killer all around the globe. Detection at an early, curable stage is essential for boosting survival rates since conventional diagnostic procedures frequently miss these phases. The conventional techniques face challenges such as limited nanosensor data, high dimensionality, real-time processing demands, and the need for interpretable yet scalable diagnostic solutions. The paper proposes NDL-ECD Nano Deep-Learning (NDL) for early cancer detection (ECD), leveraging advances in nanosensor technology and artificial intelligence. NOECD aims to develop a highly sensitive and efficient framework for detecting cancer biomarkers using deep learning techniques like CNN. These nanosensor outputs are processed by a lightweight Convolutional Neural Network (CNN) designed to extract and classify features indicative of cancer. To enhance performance, a nano-optimization module is employed for data augmentation, feature scaling, and architecture optimization, overcoming challenges such as limited data availability and computational efficiency. The proposed framework was validated using synthetic and real-world nanosensor datasets, attaining a sensitivity increase of 25% and a false positive decrease of 20% compared to traditional diagnostic methods. Additionally, the lightweight architecture ensures realtime performance, making it suitable for point-of-care and large-scale screening applications. In conclusion, NDL-ECD presents a transformative approach to early cancer detection by integrating nanosensor precision with deep learning capabilities, offering a scalable, non-invasive, and efficient diagnostic solution.