Early detection, regular monitoring of eyelid tumors and post-surgery recurrence monitoring are crucial for patients. However, frequent hospital visits are burdensome for patients with poor medical conditions. This study validates a novel deep learning-based mobile application, based on YOLOv5 and Efficient-Net v2-B architectures, for self-diagnosing eyelid tumors, enabling improved health support systems for such patients. 1195 preprocessed clinical ocular photographs and biopsy results were collected for model training. The best-performing model was chosen and converted into a smartphone-based application, then further evaluated based on external validation dataset, achieved 0.921 accuracy for triple classification outcomes (benign/malignant eyelid tumors or normal eye), generally superior to that of general physicians, resident doctors, and ophthalmology specialists. Intelligent Eyelid Tumor Screening application exhibited a straightforward detection process, user-friendly interface and treatment recommendation scheme, provides preliminary evidence for recognizing eyelid tumors and could be used by healthcare professionals, patients and caregivers for detection and monitoring purposes.