LesionTalk: Core Data Extraction and Multi-class Lesion Detection in IoT-based Intelligent Healthcare

Abstract

With the development of intelligent medicine, lesion detection supported by Internet of Things (IoT), big data, and deep learning has become a hotspot. However, lesion detection technology based on deep learning requires huge amounts of high-quality medical image data, and the data from social IoT has the problems of uneven quality and lack of lesion labeling. Current studies usually ignore the unstable quality of IoT data and the interpretability of diagnostic results, resulting in deeper model layers, larger models, poor real-time performance, and lack of persuasion. To address the problems, this article first proposes a core data extraction method for multi-class lesion detection based on unlabeled medical image from social IoT. Then, we propose an ensemble algorithm based on lightweight models to improve the detection accuracy. Finally, we visualize pathological features to enhance the interpretability of core data and detection results. The experimental results show that our method can effectively extract the core data of multiple lesions from low-quality medical images and improve the accuracy of the lightweight lesion detection model as well as the interpretation of detection results.