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.