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Laryngo-pharyngeal cancer(LPC) is a high-mortality malignancy whose early diagnosis is critical for effective treatment. However, traditional diagnostic methods rely heavily on laryngologists' expertise which will cause possible missed diagnoses or repeated biopsies. To address this, we propose the Laryngo-Pharyngeal Cancer Diagnosing System (LPC-SYS), an AI-powered system that leverages YOLO-based object detection models for real-time LPC detection from endoscopic images. LPC-SYS uses a microservices architecture to ensure scalability and efficient task management, enabling seamless integration into clinical workflows. Meanwhile, extensive experiments conducted on two real private LPC datasets demonstrate that LPC-SYS, utilizing YOLO models, achieves competitive results. With its high diagnostic accuracy and minimal delays, LPC-SYS is set to be deployed in the clinical settings of the First Affiliated Hospital, Sun Yat-sen University. This implementation will provide a reliable and scalable solution for LPC diagnosis.