Revolutionizing Healthcare Systems: Synergistic Multimodal Ensemble Learning & Knowledge Transfer for Lung Cancer Delineation & Taxonomy

Abstract

Lung cancer presents a substantial global public health concern, underscoring the crucial role of early detection in enhancing patient prognosis and well-being. This paper presents a novel deep ensemble model for the detection and classification of lung cancer, addressing the pressing issue of high incidence and mortality rates associated with the disease, utilizing transfer learning (TL) with Convolutional Neural Networks (CNNs) and integrating modern technology in the form of fitness trackers. The ensemble combines CNNs namely VGG16, VGG19, InceptionV3, Xception, and DenseNet201 through weighted voting, achieving a remarkable 97.2% accuracy. This innovation extends beyond image analysis by integrating fitness trackers that continuously monitor health metrics, enhancing patient engagement and proactive health management. The framework’s capacity to transform both the diagnosis and treatment of lung cancer is highlighted by its heightened precision and extensive patient monitoring capabilities, offering the prospect of better outcomes and more efficient healthcare delivery.