Advanced-Data Analytics in Medical Imaging: Combining AI-based Tumor Detection with Facial Recognition for Comprehensive Patient Care

Abstract

The application of artificial intelligence (AI) in medical imaging has greatly enhanced the accuracy of diagnosis and patient care. This study investigates a new method that merges AI-based tumor detection with facial recognition technology to improve holistic healthcare services. Four state-of-the-art deep learning models—CNN, ResNet-50, VGG-16, and YOLO—are used in the study for tumor detection, comparing their performance in terms of accuracy, sensitivity, specificity, and processing time. Experimental outcomes indicate that ResNet-50 had the best accuracy (96.8%), followed by VGG-16 (94.5%), CNN (92.3%), and YOLO (90.1%). Sensitivity and specificity analysis also showed the accuracy of AI in detecting tumors. Facial recognition was used to automate patient identification, reducing misidentification errors by 98%. The study results demonstrate the potential of AI-based diagnostic tools in enhancing early tumor detection and hospital operations. While the research emphasizes the advantages of AI, data privacy and algorithmic bias remain a challenge and therefore need further research. The results indicate that the combination of AI-based imaging and facial recognition can improve diagnostic accuracy, security, and overall patient management. Future research should aim at maximizing AI models for use in real-time as well as in multi-modal data fusion in a clinical setting.