Secure IoMT for Disease Prediction Empowered With Transfer Learning in Healthcare 5.0, the Concept and Case Study

Abstract

Identifying human diseases remains a difficult process, even in the age of advanced information technology and the smart healthcare industry 5.0. In the smart healthcare industry 5.0, precise prediction of human diseases, particularly lethal cancer diseases, is critical for human well-being. The global Internet of Medical Things sector has advanced at a breakneck pace in recent years, from small wristwatches to large aircraft. The critical aspects of the Internet of Medical Things include security and privacy, owing to the massive scale and deployment of the Internet of Medical Things networks. Transfer learning with a secure IoMTbased approach is considered. The google net deep machine-learning model is used for accurate disease prediction in the smart healthcare industry 5.0. We can easily and reliably anticipate the lethal cancer disease in the human body by using the secure IoMT-based transfer learning approach. Furthermore, the results of the proposed secure IoMT-based Transfer learning techniques are used to validate the best cancer disease prediction in the smart healthcare industry 5.0. The proposed secure IoMT-based transfer learning methodology reached 98.8%, better than the state-of-the-art methodologies used previously for cancer disease prediction in the smart healthcare industry 5.0.INDEX TERMS IoMT, transfer learning, deep machine learning, histopathology, image processing, lung cancer.

Birthweight Range Prediction and Classification: A Machine Learning-Based Sustainable Approach

“…Using a variety of metrics will significantly help in defining the performance of this model [35]. The equations of the previously mentioned measures are [36][37][38]:…”

Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management

“…In contrast to the studies in the literature, the proposed study investigated a middle eastern dataset while addressing the kingdom's waste management problem, which is first of its kind study in the kingdom. ResNEt34, VGG126, InceptionV3, DenseNet121, MobileNetV3, and GNet [43] 24,000 images from Huawei challenge cup dataset [24] GNet accuracy = 92.62% [33] YoloV5 [45] TrashNet Accuracy = 95.51% [37] CNN [44] TrashNet Accuracy = 92.6% [38] InceptionV3 [43] TrashNet Accuracy = 92.87% [14] EfficientdetD2 & EfficientnetB2 [43] 14,000 instances Accuracy = 75% [21] MLH-CNN, AlexNet, RestNet50, and VGG16, DL model with (DLSODC-GWM) [43] Benchmark datasets Precision = 95.23%, Recall = 94.29% F-score = 94.73% [28] CNN [44] TrashNet, and self-built dataset Accuracy = 96.77% [32] GCNet [43] Internet collected dataset combined with self-built dataset Accuracy = 97.54%.…”

Transfer Learning Approach to Seed Taxonomy: A Wild Plant Case Study

“…Several deep learning models were investigated, and it was concluded that the proposed CNN model outperformed the other models with the highest accuracy of 99.82%. In transfer learning, the knowledge gained from a pretrained model is used as a starting point for training a new model, rather than starting from scratch [26][27][28].…”

“…It can help improve the performance of a model by leveraging the knowledge gained from a pretrained model. Moreover, it can improve the generalization performance of a model, and it allows the model to learn from a larger and more diverse set of data [26][27][28]. In the context of plants, taxonomy involves classifying plants into different groups based on their morphological, physiological, and molecular characteristics.…”