Cloud of medical things (CoMT) based smart healthcare framework for resource allocation

Rajendran, Surendran; Tamilvizhi, T.

doi:10.1049/icp.2021.0855

Cited by 10 publications

(3 citation statements)

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“…The recording of many factors related to the tumor's features is required in order for these models to function, whereas generative models require prior knowledge of the tumor and its tissue and appearance to function. Models that are discriminative [10], on the other hand, become familiar with the quality of a cancer and how to segment it from physically commenting on information without the requirement for extra manual preparation (made by radiologists).…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Rajagopal¹,

Thanarajan²,

Alotaibi³

et al. 2023

Computer Systems Science and Engineering

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Automated segmentation of brain tumors using Magnetic Resonance Imaging (MRI) data is critical in the analysis and monitoring of disease development. As a result, gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods. It is intended to extract characteristics from an image using the Gray Level Co-occurrence (GLC) matrix feature extraction method described in the proposed work. Using Convolutional Neural Networks (CNNs), which are commonly used in biomedical image segmentation, CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor. Using two segmentation networks, a U-Net and a 3D CNN, we present a major yet easy combinative technique that results in improved and more precise estimates. The U-Net and 3D CNN are used together in this study to get better and more accurate estimates of what is going on. Using the dataset, two models were developed and assessed to provide segmentation maps that differed fundamentally in terms of the segmented tumour sub-region. Then, the estimates was made by two separate models that were put together to produce the final prediction. In comparison to current state-of-the-art designs, the precision (percentage) was 98.35, 98.5, and 99.4 on the validation set for tumor core, enhanced tumor, and whole tumor, respectively.

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Section: Related Workmentioning

confidence: 99%

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Rajagopal¹,

Thanarajan²,

Alotaibi³

et al. 2023

Computer Systems Science and Engineering

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“…Finally, the DSAE model receives the useful feature vectors and then classifies sugar plant leaf diseases. AE is a type of unsupervised 3-layer NN that has 2 parts of encoding and decoding, comprising input, hidden, and output layers [27].…”

Section: Image Classification Using Dsae Modelmentioning

confidence: 99%

Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification

Tamilvizhi¹,

Rajendran

Anbazhagan

et al. 2022

Mathematical Problems in Engineering

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Plant diseases pose a major challenge in the agricultural sector, which affects plant development and crop productivity. Sugarcane farming is a highly organized part of farming. Owing to the desirable condition for sugarcane cultivation, India stands among the second largest producers of sugarcane over the globe. At the same time, sugarcane gets easily affected by multifarious diseases which significantly influence crop productivity. The recently developed computer vision (CV) and deep learning (DL) models with an effective design can be employed for the detection and classification of diseases in sugarcane plant. The disease detection in sugarcane plant is not accurate in the existing techniques. This paper presents a quantum behaved particle swarm optimization based deep transfer learning (QBPSO-DTL) model for sugarcane leaf disease detection and classification which produces high accuracy. The proposed QBPSO-DTL method is designed and trained for the prediction of diseased leaf images. The proposed QBPSO-DTL technique encompasses the design of optimal region growing segmentation to determine the affected regions in the leaf image. In addition, the SqueezeNet model is employed as a feature extractor and the deep stacked autoencoder (DSAE) model is applied as a classification model. Finally, the hyperparameter tuning of the DSAE model is carried out by using the QBPSO algorithm. For demonstrating the enhanced outcomes of the QBPSO-DTL approach, a wide range of experiments were implemented and the results ensured the improvements of the QBPSO-DTL model.

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“…With respect to data exchange in a transmission system, there is authentication when the transmitter of information could be unequivocally recognized by the receiver. Consecutively, there is integrity if it could be revealed that a message or data hasn't been deleted, created, or modified by unauthorized systems or users [10].…”

Section: Introductionmentioning

confidence: 99%

Encryption with User Authentication Model for Internet of Medical Things Environment

Riya¹,

Surendran²,

Romero³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Internet of Medical Things (IoMT) enabled e-healthcare has the potential to greately improve conventional healthcare services significantly. However, security and privacy become major issues of IoMT because of the restricted processing abilities, storage, and energy constraints of the sensors. Therefore, it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors. In order to ensure security on sensitive medical data, effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers. In this view, this study designs an effective metaheuristic optimization based encryption with user authentication (EMOE-UA) technique for IoMT environment. This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity. Moreover, the EMOE-UA technique employs optimal multikey homomorphic encryption (OMKHE) technique to encrypt the IoMT data. Furthermore, the improved social spider optimization algorithm (ISSOA) was employed for the optimal multikey generation of the MKHE technique. The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects. The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

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Cloud of medical things (CoMT) based smart healthcare framework for resource allocation

Cited by 10 publications

References 0 publications

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN

Quantum Behaved Particle Swarm Optimization-Based Deep Transfer Learning Model for Sugarcane Leaf Disease Detection and Classification

Encryption with User Authentication Model for Internet of Medical Things Environment

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