RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images

Ahalya, R. K.; Almutairi, Fadiyah M.; Snekhalatha, U.; Dhanraj, Varun; Aslam, Shabnam M.

doi:10.1038/s41598-023-42111-3

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…For other forms of imaging, AI-based models for the recognition of inflammatory arthritis in the small joints of the hand and finger has already proved successful. Ahalya et al trained and validated a CNN that was able to classify thermal images of the hand of patients with RA against healthy controls with high accuracy, 33 while an approach by Phatak et al was successfully trained to detect synovitis of the wrist and finger joints based on hand photographs. 34 Regarding MRI of the hands, however, AI applications so far have been very limited and have been exclusively focused on the classification of different types of arthritis 18 or of early disease against non-arthritic controls, 19 while automatic methods for scoring arthritis severity quantitatively and based on the recognition of individual inflammatory lesions are missing.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis

Schlereth,

Mutlu,

Utz

et al. 2024

RMD Open

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ObjectivesTo train, test and validate the performance of a convolutional neural network (CNN)-based approach for the automated assessment of bone erosions, osteitis and synovitis in hand MRI of patients with inflammatory arthritis.MethodsHand MRIs (coronal T1-weighted, T2-weighted fat-suppressed, T1-weighted fat-suppressed contrast-enhanced) of rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients from the rheumatology department of the Erlangen University Hospital were assessed by two expert rheumatologists using the Outcome Measures in Rheumatology-validated RA MRI Scoring System and PsA MRI Scoring System scores and were used to train, validate and test CNNs to automatically score erosions, osteitis and synovitis. Scoring performance was compared with human annotations in terms of macro-area under the receiver operating characteristic curve (AUC) and balanced accuracy using fivefold cross-validation. Validation was performed on an independent dataset of MRIs from a second patient cohort.ResultsIn total, 211 MRIs from 112 patients (14 906 region of interests (ROIs)) were included for training/internal validation using cross-validation and 220 MRIs from 75 patients (11 040 ROIs) for external validation of the networks. The networks achieved high mean (SD) macro-AUC of 92%±1% for erosions, 91%±2% for osteitis and 85%±2% for synovitis. Compared with human annotation, CNNs achieved a high mean Spearman correlation for erosions (90±2%), osteitis (78±8%) and synovitis (69±7%), which remained consistent in the validation dataset.ConclusionsWe developed a CNN-based automated scoring system that allowed a rapid grading of erosions, osteitis and synovitis with good diagnostic accuracy and using less MRI sequences compared with conventional scoring. This CNN-based approach may help develop standardised cost-efficient and time-efficient assessments of hand MRIs for patients with arthritis.

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Section: Discussionmentioning

confidence: 99%

Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis

Schlereth,

Mutlu,

Utz

et al. 2024

RMD Open

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“…Experts refer this as achieving the quantum supremacy or quantum advantage [25]. Quantum computing may definitely revolutionize various areas such as cryptography, chemistry, finance and machine learning [26][27][28][29][30][31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Quantum Computing in The Cloud - A Systematic Literature Review

Asyraf Zhahir,

Munirah Mohd,

Ilias M Shuhud

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

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Quantum computing was proposed to simulate processes that surpass the capabilities of its counterpart, classical computing. Utilizing the principles of quantum mechanics, it improves the computing power of quantum computing. Top developers namely IBM, Rigetti, D-Wave, Qutech and Google have invested greatly in the technology. Nowadays, users can access the quantum computing system publicly over the network in a cloud environment, this system architecture is known as cloud-based quantum computing. However, different developers deliver different architecture and functionality of the system on their platforms. This has indirectly spawned a question of which cloud-based quantum computing platform is a better option based on certain specific requirements by an individual or group. The main objective of this study is to provide a proposed framework using the existing cloud-based service of quantum computing based on previous studies for users with their specific demands.

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Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques

Thirunavukkarasu,

Umapathy,

Ravi

et al. 2024

Sci Rep

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The study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visible tongue images with various fusion rules of discrete wavelet transform (DWT) to classify diabetes and normal subjects; (2) to obtain the statistical features in the required region of interest from the tongue image before and after fusion; (3) to distinguish the healthy and diabetes using fused tongue images based on deep and machine learning algorithms. The study participants comprised of 80 normal subjects and age- and sex-matched 80 diabetes patients. The biochemical tests such as fasting glucose, postprandial, Hba1c are taken for all the participants. The visible and thermal tongue images are acquired using digital single lens reference camera and thermal infrared cameras, respectively. The digital and thermal tongue images are fused based on the wavelet transform method. Then Gray level co-occurrence matrix features are extracted individually from the visible, thermal, and fused tongue images. The machine learning classifiers and deep learning networks such as VGG16 and ResNet50 was used to classify the normal and diabetes mellitus. Image quality metrics are implemented to compare the classifiers’ performance before and after fusion. Support vector machine outperformed the machine learning classifiers, well after fusion with an accuracy of 88.12% compared to before the fusion process (Thermal-84.37%; Visible-63.1%). VGG16 produced the classification accuracy of 94.37% after fusion and attained 90.62% and 85% before fusion of individual thermal and visible tongue images, respectively. Therefore, this study results indicates that fused tongue images might be used as a non-contact elemental tool for pre-screening type II diabetes mellitus.

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RANet: a custom CNN model and quanvolutional neural network for the automated detection of rheumatoid arthritis in hand thermal images

Cited by 5 publications

References 54 publications

Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis

Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis

Quantum Computing in The Cloud - A Systematic Literature Review

Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques

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