A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

Song, Weibo

doi:10.3390/info11120556

Cited by 5 publications

(3 citation statements)

References 31 publications

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“…A detailed comparative result analysis of the HBESDM-DLD with existing techniques on Heart Statlog dataset is displayed in Table 5 and Fig. 8 [37,38]. From the results, it is evident that RT model has the least outcome with the minimal accuracy of 0.76.…”

Section: Performance Validationmentioning

confidence: 98%

Hyperledger blockchain enabled secure medical record management with deep learning-based diagnosis model

Sammeta

Parthiban

2021

Complex Intell. Syst.

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In recent times, advanced developments in healthcare sector result in the generation of massive amounts of electronic health records (EHRs). EHR system enables the data owner to control his/her data and share it with designated people. The vast volume of data in the healthcare system makes it difficult for data to ensure security and diagnostic processes. To resolve these issues, this paper develops a new hyperledger blockchain enabled secure medical data management with deep learning (DL)-based diagnosis (HBESDM-DLD) model. The presented model involves distinct stages of operations such as encryption, optimal key generation, hyperledger blockchain-based secure data management, and diagnosis. The presented model allows the user to control access to data, permit the hospital authorities to read/write data, and alert emergency contacts. For encryption, SIMON block cipher technique is applied. At the same time, to improve the efficiency of the SIMON technique, a group teaching optimization algorithm (GTOA) is applied for the optimal key generation of the SIMON technique. Moreover, the sharing of medical data takes place using multi-channel hyperledger blockchain that utilizes a blockchain for storing patient visit data and for the medical institutions to record links for the EHRs saved in external databases. Once the data are decrypted at the receiving end, finally, variational autoencoder (VAE)-based diagnostic model is applied to detect the existence of the diseases. The performance validation of the HBESDM-DLD model takes place on benchmark medical dataset and the results are inspected under various performance measures. The experimental results proves that the HBESDM-DLD methodology is superior to state-of-the-art methods.

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Section: Performance Validationmentioning

confidence: 98%

Hyperledger blockchain enabled secure medical record management with deep learning-based diagnosis model

Sammeta

Parthiban

2021

Complex Intell. Syst.

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“…Further breakthroughs are expected in various medical areas, particularly in oncology, since cancer is one of the most prevalent causes of death worldwide [5,6]. Technology is projected to play a greater role in medical practice, and the application of computer-aided equipment is especially promising [7][8][9]. In particular, Computer-Aided Detection (CAD) systems are becoming an important tool in supporting physicians in cancer detection and prevention, particularly when diagnostic images are complex to analyze (due to low quality) and in a high number.…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System for Melanoma Diagnosis from Dermoscopic Images

Rízzí

Guaragnella

2022

Applied Sciences

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Innovative technologies in dermatology allow for the early screening of skin cancer, which results in a reduction in the mortality rate and surgical treatments. The diagnosis of melanoma is complex not only because of the number of different lesions but because of the high similarity amongst skin lesions of different nature; hence, human vision and physician experience still play a major role. The adoption of automatic systems would aid clinical assessment and make the diagnosis reproducible by eliminating inter- and intra-observer variabilities. In our paper, we describe a computer-aided system for the early diagnosis of melanoma in dermoscopic images. A soft pre-processing phase is performed so as to avoid the loss of details both in texture, colors, and contours, and color-based image segmentation is later carried out using k-means. Features linked to both geometric properties and color characteristics are used to analyze skin lesions through a support vector machine classifier. The PH2 public database is used for the assessment of the procedure’s sensitivity, specificity, and accuracy. A statistical approach is carried out to establish the impact of image quality on performance. The obtained results show remarkable achievements, so our computer-aided approach should be suitable as a Decision Support System for melanoma detection.

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“…In the last decades, vision-based systems are becoming increasingly important in supporting a wide range of application areas, including environment modelling for moving cameras [1][2][3][4][5], human action and event recognition [6][7][8][9], target and object detection [10][11][12][13][14][15][16]; even in areas such as medical image analysis [17][18][19][20][21][22][23], emotion or deception recognition [24][25][26][27][28][29], and immersive rehabilitation by serious games [30][31][32][33][34][35], these systems are, now, of daily use. At the same time, the last 10 years have seen substantial improvements of small-scale Unmanned Aerial Vehicles (UAVs), hereinafter UAVs, in terms of flight time, automatic control, embedded processing, and remote transmission.…”

Section: Introductionmentioning

confidence: 99%

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

et al. 2021

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In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of many of these applications thanks to its ability to identify areas and/or objects of interest without knowing them a priori. In this paper, a One-Class Support Vector Machine (OC-SVM) anomaly detector based on customized Haralick textural features for aerial video surveillance at low-altitude is presented. The use of a One-Class SVM, which is notoriously a lightweight and fast classifier, enables the implementation of real-time systems even when these are embedded in low-computational small-scale UAVs. At the same time, the use of textural features allows a vision-based system to detect micro and macro structures of an analyzed surface, thus allowing the identification of small and large anomalies, respectively. The latter aspect plays a key role in aerial video surveillance at low-altitude, i.e., 6 to 15 m, where the detection of common items, e.g., cars, is as important as the detection of little and undefined objects, e.g., Improvised Explosive Devices (IEDs). Experiments obtained on the UAV Mosaicking and Change Detection (UMCD) dataset show the effectiveness of the proposed system in terms of accuracy, precision, recall, and F1-score, where the model achieves a 100% precision, i.e., never misses an anomaly, but at the expense of a reasonable trade-off in its recall, which still manages to reach up to a 71.23% score. Moreover, when compared to classical Haralick textural features, the model obtains significantly higher performances, i.e., ≈20% on all metrics, further demonstrating the approach effectiveness.

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A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

Cited by 5 publications

References 31 publications

Hyperledger blockchain enabled secure medical record management with deep learning-based diagnosis model

Hyperledger blockchain enabled secure medical record management with deep learning-based diagnosis model

A Decision Support System for Melanoma Diagnosis from Dermoscopic Images

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

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