Automated segmentation and classification of hand thermal images in rheumatoid arthritis using machine learning algorithms: A comparison with quantum machine learning technique

Ahalya, R. K.; Snekhalatha, U.; Dhanraj, Varun

doi:10.1016/j.jtherbio.2022.103404

Cited by 18 publications

(6 citation statements)

References 29 publications

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“…The proposed study used pre-trained models such as ResNet101V2, InceptionResNetV2, and DenseNet201 to classify the RA and healthy controls 51 , 52 . The performances of these pre-trained models were computationally less effective because it relied on weights from the ImageNet dataset that resulted in negative transfer learning and overfitting 53 .…”

Section: Discussionmentioning

confidence: 99%

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

Ahalya,

Almutairi,

Snekhalatha

et al. 2023

Sci Rep

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Rheumatoid arthritis is an autoimmune disease which affects the small joints. Early prediction of RA is necessary for the treatment and management of the disease. The current work presents a deep learning and quantum computing-based automated diagnostic approach for RA in hand thermal imaging. The study’s goals are (i) to develop a custom RANet model and compare its performance with the pretrained models and quanvolutional neural network (QNN) to distinguish between the healthy subjects and RA patients, (ii) To validate the performance of the custom model using feature selection method and classification using machine learning (ML) classifiers. The present study developed a custom RANet model and employed pre-trained models such as ResNet101V2, InceptionResNetV2, and DenseNet201 to classify the RA patients and normal subjects. The deep features extracted from the RA Net model are fed into the ML classifiers after the feature selection process. The RANet model, RA Net+ SVM, and QNN model produced an accuracy of 95%, 97% and 93.33% respectively in the classification of healthy groups and RA patients. The developed RANet and QNN models based on thermal imaging could be employed as an accurate automated diagnostic tool to differentiate between the RA and control groups.

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Section: Discussionmentioning

confidence: 99%

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

Ahalya,

Almutairi,

Snekhalatha

et al. 2023

Sci Rep

Self Cite

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“…QNNs and QSCVs were applied to EHRs to classify ischemic heart disease (Maheshwari et al, 2023), while transfer learning-based QNNs were explored in the context of classifying breast cancer (Azevedo et al, 2022). Rheumatoid arthritis was detected by classifying thermal hand images with QSVCs trained via quantum kernel alignment (Ahalya et al, 2023). Alzheimer's disease was classified with MRI images using QNNs (Shahwar et al, 2022), and COVID-19 was classified with QNNs using chest X-ray (Houssein et al, 2022) as well as CT lung images (Sengupta and Srivastava, 2021;Amin et al, 2022).…”

Section: Diagnosticsmentioning

confidence: 99%

The state of quantum computing applications in health and medicine

Flöther

2023

Res. dir. Quantum technol.

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Quantum computing hardware and software have made enormous strides over the last years1. Questions around quantum computing’s impact on research and society have changed from “if” to “when/how”. The 2020s have been described as the “quantum decade”, and the first production solutions that drive scientific and business value are expected to become available over the next years. Medicine, including fields in healthcare and life sciences, has seen a flurry of quantum-related activities and experiments in the last few years (although medicine and quantum theory have arguably been entangled ever since Schrödinger’s cat2). The initial focus was on biochemical and computational biology problems3,4,5,6,7,8; recently, however, clinical and medical quantum solutions have drawn increasing interest. The rapid emergence of quantum computing in health and medicine necessitates a mapping of the landscape. In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies from the last few years. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research. Near-term QML algorithms, for instance, quantum support vector classifiers and quantum neural networks, have been trained with diverse clinical and real-world data sets. This includes studies in generating new molecular entities as drug candidates, diagnosing based on medical image classification, predicting patient persistence, forecasting treatment effectiveness, and tailoring radiotherapy. The use cases and the applied algorithms are summarized. In addition, this review provides an outlook on medicine in the quantum era. There has been much discussion about healthcare’s journey towards precision medicine and the quadruple aim (better health, lower costs, enhanced patient experiences, and improved healthcare practitioner work lives)9. While a range of technical and ethical challenges remain, quantum computing is poised to become a key enabler for advancing towards the holy grail: keeping people healthy through proactive medical care and guidance at the level of an individual.

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“…First, AI algorithms, such as naive Bayes, convolutional neural network (CNN), logistic regression, and support vector machine (SVMs) or deep learning can analyze imaging data, such as X-rays, magnetic resonance imaging (MRI), and computer tomography (CT), to detect subtle changes in the joints that may indicate early RA. [ 10 , 11 ] MRI can provide accurate data on early signs of inflammatory arthritis, particularly in the identification of bone marrow edema and tenosynovitis. [ 12 ] X-rays is also most commonly used and easily accessible non-invasive method for monitoring disease progression, and can also detect bone structural changes in early stages.…”

Section: Ai In the Diagnosis Of Ramentioning

confidence: 99%

A survey of artificial intelligence in rheumatoid arthritis

Wang

Tian

Zhou

et al. 2023

Rheumatology and Immunology Research

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The article offers a survey of currently notable artificial intelligence methods (released between 2019-2023), with a particular emphasis on the latest advancements in detecting rheumatoid arthritis (RA) at an early stage, providing early treatment, and managing the disease. We discussed challenges in these areas followed by specific artificial intelligence (AI) techniques and summarized advances, relevant strengths, and obstacles. Overall, the application of AI in the fields of RA has the potential to enable healthcare professionals to detect RA at an earlier stage, thereby facilitating timely intervention and better disease management. However, more research is required to confirm the precision and dependability of AI in RA, and several problems such as technological and ethical concerns related to these approaches must be resolved before their widespread adoption.

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Automated segmentation and classification of hand thermal images in rheumatoid arthritis using machine learning algorithms: A comparison with quantum machine learning technique

Cited by 18 publications

References 29 publications

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

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

The state of quantum computing applications in health and medicine

A survey of artificial intelligence in rheumatoid arthritis

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