A generalized deep learning framework for automatic rheumatoid arthritis severity grading

More, Sujeet; Singla, Jimmy

doi:10.3233/jifs-212015

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…Following that, the computer will arrive at a conclusion by doing several assessments. The expert system is computer software designed from intelligent machines that is best suited for real-time domains or applications [ 41 , 42 ]. This domain is developed to identify real-world applications in more than 70 percent of cases [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

Murugesan

Ahmed

Bhola

et al. 2022

BioMed Research International

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Kidney failure occurs whenever the kidney stops to operate properly and would be unable to cleanse or refine the bloodstream as it should. Chronic kidney disease (CKD) is a potentially fatal consequence. If this condition is diagnosed early, its progression can be delayed. There are various factors that increase the likelihood of developing kidney failure. As a consequence, in order to detect this potentially fatal condition early on, these risk factors must be checked on a regular basis before the individual’s health deteriorates. Furthermore, it lowers the cost of therapy. The chronic kidney or renal disease will be recognized in this work utilizing fuzzy and adaptive neural fuzzy inference systems. The fundamental purpose of this initiative is to enhance the precision of medical diagnostics used to diagnose illnesses. Nephron functioning, glucose levels, systolic and diastolic blood pressure, maturity level, weight and height, and smoking are all elements to consider while developing a fuzzy and adaptable neural fuzzy inference system. The output variable describes a specific patient’s stage of chronic renal disease based on input factors such as stage 1, stage 2, stage 3, stage 4, and stage 5. The outcome will show the present stage of a patient’s kidney. As a result, these methods can assist specialists in determining the stage of chronic renal disease. MATLAB software is used to create the fuzzy and neural fuzzy inference systems.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

Murugesan

Ahmed

Bhola

et al. 2022

BioMed Research International

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“…More et al ( 22 ) introduced a DL model named ResNet50 for assessing the severity of RA in the knee, relying on the Kelgren Lawrence (KL) classification system, using MRI T1W, T2W and PD sequences. The study demonstrated a high level of accuracy at 96,85%, precision of 98,31%, area under curve (AUC) of 0,98 and a mean absolute error (MAE) of 0,015.…”

Section: Applications Of Artificial Intelligence Based On Mri In Rheu...mentioning

confidence: 99%

“…The fundamental stages of an AI algorithm are illustrated in Figure 2 . From the image acquisition to the last stage of result analysis, image processing is essential, undergoing multiple stages before reaching a diagnosis ( 22 ).…”

Section: Introductionmentioning

confidence: 99%

Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review

Nicoara,

Sas,

Bita

et al. 2023

Front. Med.

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Early diagnosis in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is essential to initiate timely interventions, such as medication and lifestyle changes, preventing irreversible joint damage, reducing symptoms, and improving long-term outcomes for patients. Since magnetic resonance imaging (MRI) of the wrist and hand, in case of RA and MRI of the sacroiliac joints (SIJ) in case of axSpA can identify inflammation before it is clinically discernible, this modality may be crucial for early diagnosis. Artificial intelligence (AI) techniques, together with machine learning (ML) and deep learning (DL) have quickly evolved in the medical field, having an important role in improving diagnosis, prognosis, in evaluating the effectiveness of treatment and monitoring the activity of rheumatic diseases through MRI. The improvements of AI techniques in the last years regarding imaging interpretation have demonstrated that a computer-based analysis can equal and even exceed the human eye. The studies in the field of AI have investigated how specific algorithms could distinguish between tissues, diagnose rheumatic pathology and grade different signs of early inflammation, all of them being crucial for tracking disease activity. The aim of this paper is to highlight the implementation of AI models in MRI with focus on diagnosis of RA and axSpA through a literature review.

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“…In contrast to the basic algorithm, simulation investigation revealed that the hybrid VM allocation saved 72 percent of power when delivering quality service [13,14]. Jiang [15] suggested a data ABC (Ant Bee Colony) energy model for making VM consolidation decisions based on global optimization.…”

Section: Related Workmentioning

confidence: 99%

Machine-Learning-Based Approach for Virtual Machine Allocation and Migration

et al. 2022

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Due to its ability to supply reliable, robust and scalable computational power, cloud computing is becoming increasingly popular in industry, government, and academia. High-speed networks connect both virtual and real machines in cloud computing data centres. The system’s dynamic provisioning environment depends on the requirements of end-user computer resources. Hence, the operational costs of a particular data center are relatively high. To meet service level agreements (SLAs), it is essential to assign an appropriate maximum number of resources. Virtualization is a fundamental technology used in cloud computing. It assists cloud providers to manage data centre resources effectively, and, hence, improves resource usage by creating several virtualmachine (VM) instances. Furthermore, VMs can be dynamically integrated into a few physical nodes based on current resource requirements using live migration, while meeting SLAs. As a result, unoptimised and inefficient VM consolidation can reduce performance when an application is exposed to varying workloads. This paper introduces a new machine-learning-based approach for dynamically integrating VMs based on adaptive predictions of usage thresholds to achieve acceptable service level agreement (SLAs) standards. Dynamic data was generated during runtime to validate the efficiency of the proposed technique compared with other machine learning algorithms.

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A generalized deep learning framework for automatic rheumatoid arthritis severity grading

Cited by 9 publications

References 24 publications

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

[Retracted] Fuzzy Logic‐Based Systems for the Diagnosis of Chronic Kidney Disease

Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review

Machine-Learning-Based Approach for Virtual Machine Allocation and Migration

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