An MIoT Framework of Consumer Technology for Medical Diseases Prediction
Sudeshna Pattanaik, Chinmay Chakraborty, Subhasikta Behera, Santosh Kumar Majhi, Subhendu Kumar Pani
Abstract
The healthcare sector has evolved by integrating consumer technologies, IoT, and deep learning. IoT in healthcare includes connected-health, smart-health, and mobile-health, enabling devices to share information for better care. Deep learning, particularly in medical imaging, shows promise for future medical applications. A recent study proposed a hybrid model using Stacked BiLSTM with Resnet50 Model and Adaswarm optimizer to classify medical disorders from five image datasets collected from consumer devices. These datasets, including COVID-19, Pneumonia, Malaria, lung cancer, and Brain Tumor, were employed to train the model. The dataset collected by sensors are sent to the cloud for sorting through a gateway. In this IoT framework, more consumer electronic products like microcontrollers and sockets are used in consumer devices. The proposed meta-heuristic algorithm-based model achieved an impressive accuracy of 99% with an average loss of 0.019. Additionally, the study compared this model with existing prototypes across various classification measures, demonstrating its efficacy.