FS3DCIoT: A Few-Shot Incremental Learning Network for Skin Disease Differential Diagnosis in the Consumer IoT

The computer-aided diagnosis (CAD) method based on few-shot learning (FSL) effectively reduces the dependence on labelled medical images. However, the catastrophic forgetting defect of neural networks seriously decreases the ability of CAD methods to meet real diagnosis needs. Moreover, the low classification accuracy and limited categories make FSL classification difficult. A few-shot class incremental learning (FSCIL) method for skin disease diagnosis (SDD) based on the consumer Internet of Things (CIoT) is designed to solve these problems in this paper. First, dermoscopic images and clinical images obtained from CIoT nodes are used to cover more skin disease categories, and a dual-flow modal alignment module is designed to mitigate the modal misalignment of different modal images. Second, a queue of gradient episodic memory (Q-GEM) method is designed to solve the catastrophic forgetting problem. Third, a differential diagnosis method (DDM), which can effectively improve the low classification accuracy of the few-shot learning (FSL) classification network, is designed. Experiments show that the top-3 diagnostic accuracy of the proposed method can match the accuracy level of dermatologists, the accuracy is 11.2% improvement over the SOTA method.