A novel medical image data protection scheme for smart healthcare system

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning systems.Smart healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical data.In smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance Imaging.The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI.Moreover,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission delays.To address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory.The results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,respectively.This validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

Lungs are a vital human body organ,and different Obstructive Lung Diseases(OLD)such as asthma,bronchitis,or lung cancer are caused by shortcomings within the lungs.Therefore,early diagnosis of OLD is crucial for such patients suffering from OLD since,after early diagnosis,breathing exercises and medical precautions can effectively improve their health state.A secure non-invasive early diagnosis of OLD is a primordial need,and in this context,digital image processing supported by Artificial Intelligence(AI)techniques is reliable and widely used in the medical field,especially for improving early disease diagnosis.Hence,this article presents an AIbased non-invasive and secured diagnosis for OLD using physiological and iris features.This research work implements different machine-learning-based techniques which classify various subjects,which are healthy and effective patients.The iris features include gray-level run-length matrix-based features,gray-level co-occurrence matrix,and statistical features.These features are extracted from iris images.Additionally,ten different classifiers and voting techniques,including hard and soft voting,are implemented and tested,and their performances are evaluated using several parameters,which are precision,accuracy,specificity,F-score,and sensitivity.Based on the statistical analysis,it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%.

Emotion Recognition from Occluded Facial Images Using Deep Ensemble Model

Facial expression recognition has been a hot topic for decades,but high intraclass variation makes it challenging.To overcome intraclass variation for visual recognition,we introduce a novel fusion methodology,in which the proposed model first extract features followed by feature fusion.Specifically,RestNet-50,VGG-19,and Inception-V3 is used to ensure feature learning followed by feature fusion.Finally,the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification.The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy.To evaluate the efficiency of the proposed model,we use two wild benchmark datasets Real-world Affective Faces Database(RAF-DB)and AffectNet for facial expression recognition.The proposed model classifies the emotions into seven different categories namely:happiness,anger,fear,disgust,sadness,surprise,and neutral.Furthermore,the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost,convergence and accuracy based on a standard problem specific to classification applications.