Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems

Khalil, Ali; Ibrahim, Fatma E.; Abbass, Mohammed Y.; Haggag, Nehad; Mahrous, Yasser; Seddik, Ahmad; Elsherbeeny, Zeinab; Khalaf, Ashraf A. M.; Rihan, Mohamad; El‐Shafai, Walid; El‐Banby, Ghada M.; Soltan, Eman; Soliman, Naglaa F.; Algarni, Abeer D.; Al-Hanafy, Waleed; El‐Fishawy, Adel S.; El‐Rabaie, El‐Sayed M.; Al-Nuaimy, Waleed; Dessouky, Moawad I.; Saleeb, Adel A.; Messiha, Nagy W.; El-Dokany, Ibrahim M.; El‐Bendary, Mohsen A. M.; El‐Samie, Fathi E. Abd

doi:10.1002/cnm.3530

Cited by 13 publications

(5 citation statements)

References 30 publications

(47 reference statements)

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“…The experiments were conducted using several feature sets with a different number of features, i.e., 5,10,15,20,25,30,35,40, and 45 features. Several machine learning classifiers were used, namely Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), and Long Short-Term Memory (LSTM).…”

Section: Resultsmentioning

confidence: 99%

“…Feature selection techniques play an important role in improving the performance of intrusion detection systems (IDS) in the Internet of Medical Things (IoMT) [ 25 , 35 ]. There are various feature selection algorithms that have been used in the literature to reduce the dimensionality of the input features while preserving their relevant information [ 36 ].…”

Section: Related Workmentioning

confidence: 99%

“…The use of deep learning algorithms for anomaly-based intrusion detection systems (IDS) in the Internet of Medical Things (IoMT) has gained significant attention in recent years [ 35 ]. Anomaly-based IDS aim to detect security incidents by identifying deviations from normal behavior in the network or devices [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

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An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

Alalhareth,

Hong

2023

Sensors

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In healthcare, the Internet of Things (IoT) is used to remotely monitor patients and provide real-time diagnoses, which is referred to as the Internet of Medical Things (IoMT). This integration poses a risk from cybersecurity threats that can harm patient data and well-being. Hackers can manipulate biometric data from biosensors or disrupt the IoMT system, which is a major concern. To address this issue, intrusion detection systems (IDS) have been proposed, particularly using deep learning algorithms. However, developing IDS for IoMT is challenging due to high data dimensionality leading to model overfitting and degraded detection accuracy. Feature selection has been proposed to prevent overfitting, but the existing methods assume that feature redundancy increases linearly with the size of the selected features. Such an assumption does not hold, as the amount of information a feature carries about the attack pattern varies from feature to feature, especially when dealing with early patterns, due to data sparsity that makes it difficult to perceive the common characteristics of selected features. This negatively affects the ability of the mutual information feature selection (MIFS) goal function to estimate the redundancy coefficient accurately. To overcome this issue, this paper proposes an enhanced feature selection technique called Logistic Redundancy Coefficient Gradual Upweighting MIFS (LRGU-MIFS) that evaluates candidate features individually instead of comparing them with common characteristics of the already-selected features. Unlike the existing feature selection techniques, LRGU calculates the redundancy score of a feature using the logistic function. It increases the redundancy value based on the logistic curve, which reflects the nonlinearity of the relationship of the mutual information between features in the selected set. Then, the LRGU was incorporated into the goal function of MIFS as a redundancy coefficient. The experimental evaluation shows that the proposed LRGU was able to identify a compact set of significant features that outperformed those selected by the existing techniques. The proposed technique overcomes the challenge of perceiving common characteristics in cases of insufficient attack patterns and outperforms existing techniques in identifying significant features.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

Alalhareth,

Hong

2023

Sensors

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“…Finally, with the advent of the Big Data Era and the development of data mining techniques, deep learning-based medical anomaly detection draws more attention [ 19 ]. Some updated Artificial Intelligence (AI) algorithms such as convolution neural networks might improve the model’s accuracy in future research [ 45 ].…”

Section: Discussionmentioning

confidence: 99%

Determining Association between Lung Cancer Mortality Worldwide and Risk Factors Using Fuzzy Inference Modeling and Random Forest Modeling

Denise

Zhan

et al. 2022

IJERPH

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Lung cancer remains the leading cause for cancer mortality worldwide. While it is well-known that smoking is an avoidable high-risk factor for lung cancer, it is necessary to identify the extent to which other modified risk factors might further affect the cell’s genetic predisposition for lung cancer susceptibility, and the spreading of carcinogens in various geographical zones. This study aims to examine the association between lung cancer mortality (LCM) and major risk factors. We used Fuzzy Inference Modeling (FIM) and Random Forest Modeling (RFM) approaches to analyze LCM and its possible links to 30 risk factors in 100 countries over the period from 2006 to 2016. Analysis results suggest that in addition to smoking, low physical activity, child wasting, low birth weight due to short gestation, iron deficiency, diet low in nuts and seeds, vitamin A deficiency, low bone mineral density, air pollution, and a diet high in sodium are potential risk factors associated with LCM. This study demonstrates the usefulness of two approaches for multi-factor analysis of determining risk factors associated with cancer mortality.

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“…DL has been increasingly popular on the Internet of Medical Things (IoMT) in recent years [ 25 ]. As a result, the IoMT concept is suitable for building embedded technologies that can accurately diagnose diseases in the same manner that professionals perform.…”

Section: Related Workmentioning

confidence: 99%

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

Mabrouk

Dahou

Elaziz

et al. 2022

Computational Intelligence and Neuroscience

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The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being investigated and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images’ classification that may be used anywhere, i.e., it is an ubiquitous approach. It was designed in two stages: first, we employ a transfer learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the chaos game optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell datsets. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.

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Efficient anomaly detection from medical signals and images with convolutional neural networks for Internet of medical things (IoMT) systems

Cited by 13 publications

References 30 publications

An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

An Improved Mutual Information Feature Selection Technique for Intrusion Detection Systems in the Internet of Medical Things

Determining Association between Lung Cancer Mortality Worldwide and Risk Factors Using Fuzzy Inference Modeling and Random Forest Modeling

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

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