Multi-Layered Non-Local Bayes Model for Lung Cancer Early Diagnosis Prediction with the Internet of Medical Things

Abstract

The Internet of Things (IoT) has been influential in predicting major diseases in current practice. The deep learning (DL) technique is vital in monitoring and controlling the functioning of the healthcare system and ensuring an effective decision-making process. In this study, we aimed to develop a framework implementing the IoT and DL to identify lung cancer. The accurate and efficient prediction of disease is a challenging task. The proposed model deploys a DL process with a multi-layered non-local Bayes (NL Bayes) model to manage the process of early diagnosis. The Internet of Medical Things (IoMT) could be useful in determining factors that could enable the effective sorting of quality values through the use of sensors and image processing techniques. We studied the proposed model by analyzing its results with regard to specific attributes such as accuracy, quality, and system process efficiency. In this study, we aimed to overcome problems in the existing process through the practical results of a computational comparison process. The proposed model provided a low error rate (2%, 5%) and an increase in the number of instance values. The experimental results led us to conclude that the proposed model can make predictions based on images with high sensitivity and better precision values compared to other specific results. The proposed model achieved the expected accuracy (81%, 95%), the expected specificity (80%, 98%), and the expected sensitivity (80%, 99%). This model is adequate for real-time health monitoring systems in the prediction of lung cancer and can enable effective decision-making with the use of DL techniques.

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

“…The limitations of the model are as follows: quality of outcomes based on the noise levels in the images affect the performance measures; integrating the relevant features are also an important requirement for the classification in the proposed model. The future research directions of the proposed model are as follows: enhancement using recent soft computing components and better recommender systems will be developed based on the features of datasets [ 19 , 20 , 44 , 45 , 46 , 47 , 48 ]; large datasets will be considered for validation to further improve performance measures using soft computing within the minimal computing time [ 25 , 26 , 27 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 ].…”

Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression

“…The improvement in transmission time is directly proportionate to the compression ratio employed. The compression ratio also linearly reduces the storage needed; for example, a 15:1 compression ratio would reduce the 15 MB research to just 1 MB [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ].…”

“…The entropy, run length, and dictionary-based compression techniques were developed to achieve lossless compression [ 29 , 30 , 31 , 32 , 33 ]. The DNN and principal component analysis strategies were applied to predict data compression using entropy values.…”

Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme

“…Some of these data items contain user-specific information such as habits, location and behavior. Since the collected data are exchanged over the public channels, they are susceptible to attacks 15 – 17 . In addition, some sensors and drones are placed in unattended environment but accessible locations and hence can be physically captured by the attackers 18 .…”