Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Lahoura, Vivek; Singh, Harpreet; Aggarwal, Ashutosh; Sharma, Bhisham; Mohammed, Mazin Abed; Damaševičius, Robertas; Kadry, Seifedine; Cengiz, Korhan

doi:10.3390/diagnostics11020241

Cited by 126 publications

(56 citation statements)

References 69 publications

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“…These studies suggested a method to protect sensor data and enhances the performance of big-data analysis in the system. Dynamic and adaptive optimization heuristics, such as genetic algorithm and reinforcement aware schemes, were suggested [2,5,23,[25][26][27]. The different objectives were obtained, such as the cost, security, response time and energy of sensors devices during offloading and scheduling in the IoMT network.…”

Section: Related Workmentioning

confidence: 99%

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Lakhan

Mohammed

Rashid

et al. 2021

Sensors

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The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.

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Section: Related Workmentioning

confidence: 99%

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Lakhan

Mohammed

Rashid

et al. 2021

Sensors

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“…Recurrent Neural Network (R.N.N.) can attach hidden layers and previous ones circularly [40][41][42][43][44][45][46][47][48]. These R.N.N.…”

Section: Fuzzy Weight Based Recurrent Neural Network (Fwrnn)mentioning

confidence: 99%

Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer

Senthilkumar¹,

Ramakrishnan²,

Frnda

et al. 2021

IEEE Access

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IoT has facilitated predominant advancements in cancer research in incorporating Artificial intelligence (AI) that enables the human decision makers to achieve better decision. Cervical cancer being a significant cause of mortalities among women across the world. Recently, Least Absolute Shrinkage and Selection Operator (LASSO) classifier has launched in predicting recurrence cancer genes in the cervix. However, the optimal selection of genes or recurrence genes in the prediction becomes a challenging task. Hilbert-Schmidt independence criterion with Diversity based Artificial Fish Swarm (H.S.D.A.F.S.) is phased for gene selection in the recurrence prediction to solve this paradigm. At the initial phase, the recurrence gene expression of lncRNA is collected from Geo Datasets. Secondly, data imputation, accomplished with Mode and Mean Missing method (MMM-DI). Thirdly, feature selection is compassed using H.S.D.A.F.S. In the H.S.D.A.F.S. algorithm, the diversity parameter is added based on the gene value and their risk score of the lncRNAs is computed using Artificial intelligence (AI) technique. Finally, recurrence prediction, an ENSemble Classification Framework (E.N.S.C.F.), is proposed by integrating the Internet of Things (IoT) based recurrent neural networks. The results are then combined via weighted majority voting-the prognostic factor computed with a risk score of nine lncRNA signatures for 300 samples taken from GSE44001. The Chi-Square method has pursued to obtain statistical results. The survival of the patient with recurrence cervical cancer is also portrayed in the proposed model.

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“…A distance-based computation is done for internal markers and morphological dilation was applied for the external marker. In recent times authors in [ 15 ] presented an extreme learning machine (ELM) model for the prognosis of breast cancer. In addition, a gain ratio feature selection method is deployed to remove insignificant features.…”

Section: Related Workmentioning

confidence: 99%

A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT

Gupta¹,

Sharma

Ahmed

et al. 2021

Neural Process Lett

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The unavailability of appropriate mechanisms for timely detection of diseases and successive treatment causes the death of a large number of people around the globe. The timely diagnosis of grave diseases like different forms of cancer and other life-threatening diseases can save a valuable life or at least extend the life span of an afflicted individual. The advancement of the Internet of Medical Things (IoMT) enabled healthcare technologies can provide effective medical facilities to the population and contribute greatly towards the recuperation of patients. The usage of IoMT in the diagnosis and study of histopathological images can enable real-time identification of diseases and corresponding remedial actions can be taken to save an affected individual. This can be achieved by the use of imaging apparatus with the capacity of auto-analysis of captured images. However, most deep learning-based image classifying models are bulk in size and are inappropriate for use in IoT based imaging devices. The objective of this research work is to design a deep learning-based lightweight model suitable for histopathological image analysis with appreciable accuracy. This paper presents a novel lightweight deep learning-based model "ReducedFireNet", for auto-classification of histopathological images. The proposed method attained a mean accuracy of 96.88% and an F1 score of 0.968 on evaluating an actual histopathological image data set. The results are encouraging, considering the complexity of histopathological images. In addition to the high accuracy the lightweight design (size in few KBs) of the ReducedFireNet model, makes it suitable for IoMT imaging equipment. The simulation results show the proposed model has computational requirement of 0.201 GFLOPS and has a mere size of only 0.391 MB.

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Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Cited by 126 publications

References 69 publications

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Smart-Contract Aware Ethereum and Client-Fog-Cloud Healthcare System

Incorporating Artificial Fish Swarm in Ensemble Classification Framework for Recurrence Prediction of Cervical Cancer

A Novel Lightweight Deep Learning-Based Histopathological Image Classification Model for IoMT

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