Automatic identification of bone erosions in rheumatoid arthritis from hand radiographs based on deep convolutional neural network

Murakami, Seiichi; Hatano, Kazuhiro; Tan, Joo Kooi; Kim, Hyoungseop; Aoki, Takatoshi

doi:10.1007/s11042-017-5449-4

Cited by 49 publications

(35 citation statements)

References 24 publications

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“…In [5,6], joint space narrowing estimation has been very much contemplated and accomplished a superb evaluation in RA development. e MSGVF division calculation which is predominantly utilized for disintegration of bones and different CNN structures in [7][8][9], was assumed a basic part as it presents a recognizable proof and assessment of RA. e production of the different CNN models to identify RA has been discussed in [8][9][10] which quickly gives us a new beginning for the examination of RA.…”

Section: Related Workmentioning

confidence: 99%

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Mate

Kureshi²,

Singh

2021

Journal of Healthcare Engineering

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Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.

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Section: Related Workmentioning

confidence: 99%

An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Mate

Kureshi²,

Singh

2021

Journal of Healthcare Engineering

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“…The ROC curve is used to determine the area under the curve for the appropriate classification or differentiation of images based on the category defined. 33,34 Thus, the ultrasound images are subjected to all image processing techniques like pre-processing, filtering, thresholding and morphological operations to separate and isolate skin, bone, joint and synovial regions from the image. After segregation of synovial region from the image, the segmented region is subjected to classification using deep learning.…”

Section: Deep Learningmentioning

confidence: 99%

Automatic localization of anatomical regions in medical ultrasound images of rheumatoid arthritis using deep learning

Hemalatha

Vijaybaskar

Thamizhvani

2019

Proc Inst Mech Eng H

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The pace of population aging is growing faster worldwide. The quality of life of the aging population is mostly affected by rheumatic diseases. With the increasing rate of rheumatoid arthritis in the aging population, technological advances in the field of automatic image processing and analysis have paved way for automatic detection and diagnosis of arthritis based on how the grade of the synovial region is designed. The proposed method is based on spatial analysis using intensity-based approach to segment the skin border, thresholding and connectivity algorithm for bone region segmentation, hit-or-miss transform for bone line segmentation and distance measure with image profile to detect the joint region. After this process of localization, the synovial region is determined using the active contour technique. In arthritis condition, synovitis also occurs which is categorized into four different grades based on the fluid expansion in the synovial region. The different grades are defined and analyzed through deep learning. Convolutional neural network in a deep learning algorithm is used to diagnose the particular grade of synovitis to describe the nature of arthritis. With these results, a module to detect the nature of arthritis automatically is defined.

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“…For the erosion detection part, with the extracted boundary curve, they model the erosion detection as a classification problem where they use a learned Adaboost classifier to classify points along the curve into erosion versus non-erosion. The other two methods [10], [11] also follow this pipeline with some modifications. Murakami et al [10] use a deep convolution neural network (CNN) as the classifier.…”

Section: Introductionmentioning

confidence: 99%

“…The other two methods [10], [11] also follow this pipeline with some modifications. Murakami et al [10] use a deep convolution neural network (CNN) as the classifier. Ren et al [11] use the level set-based ACM and manual refinement to generate labels for training the segmentation CNN and the Siamese Network to alleviate category imbalance for training the classification models.…”

Section: Introductionmentioning

confidence: 99%