IoMT in Low-Cost Autofluorescence Imaging and Automated Detection at Edge for Cervical Cancer

This work proposes an Internet of Medical Things (IoMT) framework to offer a low-cost, portable, and effective solution for early cervical cancer screening through an intelligent integration of autofluorescence (AF) imaging microscopy, cloud server and edge computing as software-defined network (SDN) and deep learning (DL) techniques. Wavelet transform, on red, green and blue channel of AF images are done followed by the fusion techniques that highlight various cytological details and to mitigate data scarcity problem autoencoder based Generative Adversarial Network (GAN) is employed. Finally, two DL classifier models of Convolutional Neural Network (CNN) architectures, namely VGG16 and ResNet50 are trained for cervical cancer detection on the fused images at SDN controller along with other computation extensive image processing operations. Once the DL network is trained, a pre-trained submodel is run for AF test images on edge node accessed by patients’ smart phones (mobile apps). A large set of results show that along with low latency and energy consumption, ResNet50 model achieves an accuracy of 0.99, outperforming the existing methods.