A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Khalid, Hafsa; Hussain, Muzammil; Ghamdi, Mohammed A. Al; Khalid, Tayyaba; Khalid, K. Almuzaini; Khan, Muhammad Adnan; Fatima, Kalsoom; Masood, Khalid; Almotiri, Sultan H.; Farooq, Muhammad Shoaib; Ahmed, Aqsa

doi:10.3390/diagnostics10080518

Cited by 73 publications

(28 citation statements)

References 41 publications

(45 reference statements)

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“…A lot of prior studies involving radiographic image segmentation of the human knee have only focused on knee osteoarthritis assessment [ 25 , 26 ] or knee bone detection [ 27 , 28 ]. However, there is very little research applying radiographic images to segment knee bone tumors: George et al [ 29 ] used various texture features of radiography to recognize bone patterns in the tumor region.…”

Section: Appendix A1 Tumor Detectionmentioning

confidence: 99%

Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection

Jung

Yang

et al. 2021

Diagnostics

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Tumor classification and segmentation problems have attracted interest in recent years. In contrast to the abundance of studies examining brain, lung, and liver cancers, there has been a lack of studies using deep learning to classify and segment knee bone tumors. In this study, our objective is to assist physicians in radiographic interpretation to detect and classify knee bone regions in terms of whether they are normal, begin-tumor, or malignant-tumor regions. We proposed the Seg-Unet model with global and patched-based approaches to deal with challenges involving the small size, appearance variety, and uncommon nature of bone lesions. Our model contains classification, tumor segmentation, and high-risk region segmentation branches to learn mutual benefits among the global context on the whole image and the local texture at every pixel. The patch-based model improves our performance in malignant-tumor detection. We built the knee bone tumor dataset supported by the physicians of Chonnam National University Hospital (CNUH). Experiments on the dataset demonstrate that our method achieves better performance than other methods with an accuracy of 99.05% for the classification and an average Mean IoU of 84.84% for segmentation. Our results showed a significant contribution to help the physicians in knee bone tumor detection.

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Section: Appendix A1 Tumor Detectionmentioning

confidence: 99%

Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection

Jung

Yang

et al. 2021

Diagnostics

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“…e improved model enhanced the correlation between these two by adding an intersection over the union (IoU) prediction loss branch. Deep learning rose to its prominent position in 2 Complexity digital image processing and computer vision when neural networks were applied in various types of medical image analysis datasets [27,28]. Recently, an approach [29] has been published; in this study, the author develops an automated system for stomata detection, which can detect individual stomata boundaries regardless of the plant species.…”

Section: Related Workmentioning

confidence: 99%

Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning

et al. 2021

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Stomata are the main medium of plants for the trade of water, regulate the gas exchange, and are responsible for the process of photosynthesis and transpiration. The stomata are surrounded by guard cells, which help to control the rate of transpiration by opening and closing the stomata. The stomata states (open and close) play a significant role in describing the plant’s health. Moreover, stomata counting is important for scientists to investigate the numbers of stomata that are open and those that are closed to measure their density and distribution on the surface of leaves through different sampling techniques. Although a few techniques for stomata counting have been proposed, these approaches do not identify and classify the stomata based on their states in leaves. In this research, we have developed an automatic system for stomata state identification and counting in quinoa leaf images through the transformed learning (neural network model Single Shot Detector) approach. In leaf imprint, the state of stomata has been determined by measuring the correlation between the area of stomata and the aperture of each detected stoma in the image. The stomata states have been classified through the Support Vector Machine (SVM) algorithm. The overall identification and classification accuracy of the proposed system are 98.6% and 97%, respectively, helping researchers to obtain accurate stomatal state information for leaves in an efficient and simple way.

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“…Interestingly, several deep learning algorithms had been used on adult X-ray images [13][14][15][16]. Meanwhile, very little research was conducted for medical image data collected for children [17].…”

Section: Related Workmentioning

confidence: 99%

Detection of Pediatric Femur Configuration on X-ray Images

Drazkowska

2021

Applied Sciences

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In this paper, we present a fully automatic solution for denoting bone configuration on two-dimensional images. A dataset of 300 X-ray images of children’s knee joints was collected. The strict experimental protocol established in this study increased the difficulty of post-processing. Therefore, we tackled the problem of obtaining reliable information from medical image data of insufficient quality. We proposed a set of features that unambiguously denoted configuration of the bone on the image, namely the femur. It was crucial to define the features that were independent of age, since age variability of subjects was high. Subsequently, we defined image keypoints directly corresponding to those features. Their positions were used to determine the coordinate system denoting femur configuration. A complex keypoint detector was proposed, composed of two different estimator architectures: gradient-based and based on the convolutional neural network. The positions of the keypoints were used to determine the configuration of the femur on each image frame. The overall performance of both estimators working in parallel was evaluated using X-ray images from the publicly available LERA dataset.

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A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Cited by 73 publications

References 41 publications

Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection

Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection

Stomatal State Identification and Classification in Quinoa Microscopic Imprints through Deep Learning

Detection of Pediatric Femur Configuration on X-ray Images

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