A generalized deep learning framework for automatic rheumatoid arthritis severity grading

More, Sujeet; Singla, Jimmy

doi:10.3233/jifs-212015

Cited by 7 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

Fuzzy Logic-Based Systems for the Diagnosis of Chronic Kidney Disease

Murugesan

Ahmed

Bhola

et al. 2022

BioMed Research International

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic-Based Systems for the Diagnosis of Chronic Kidney Disease

Murugesan

Ahmed

Bhola

et al. 2022

BioMed Research International

Self Cite

View full text Add to dashboard Cite

show abstract

“…Still, measuring how well a therapy works greatly depends on how the patient and the clinician feel about it. Aggressive disease-modifying therapy can alleviate symptoms and avoid irreversible joint deterioration [ 6 ]. Patients with RA who are getting good medical care may still develop musculoskeletal problems, such as damage to the knee joints that cannot be fixed.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA

et al. 2023

View full text Add to dashboard Cite

We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior–posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models.

show abstract