Performance Enhancement of Cancer Detection and Classification Using Deep Learning Approach

Abstract

Cancer detection at an early stage is critical for improving patient outcomes and reducing mortality rates. This paper presents a real-time cancer detection system that leverages deep learning algorithms in combination with advanced imaging techniques such as fluorescence imaging and algorithm like VGG 16 and Resnet 50 to analyze the input data better. The Proposed system is trained on extensive datasets of medical images, enabling it to recognize abnormalities and detect cancerous lesions in their earliest stages with high accuracy. Furthermore, the adaptive learning capabilities of the deep learning models ensure continuous improvement in prediction accuracy over time. This innovative approach not only enhances early-stage cancer detection and treatment but also reduces healthcare costs and diagnostic waiting periods. By addressing critical challenges in healthcare, this system demonstrates the transformative potential of deep learning in medical applications.