Faster Region-Convolutional Neural network oriented feature learning with optimal trained Recurrent Neural Network for bone age assessment for pediatrics

Deshmukh, Sonal; Khaparde, Arti

doi:10.1016/j.bspc.2021.103016

Cited by 16 publications

(9 citation statements)

References 32 publications

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“…In ( 44 ), a TW-based DL system combines an RNN (with a modified optimization process) as a classifier with an FR-CNN (Faster Region-CNN) as a feature extractor. This hybrid model was tested using public and private datasets, with better results than models based on single architecture and non-modified optimization process.…”

Section: Application Of Deep Learning In Endocrinologymentioning

confidence: 99%

An overview of deep learning applications in precocious puberty and thyroid dysfunction

2022

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In the last decade, deep learning methods have garnered a great deal of attention in endocrinology research. In this article, we provide a summary of current deep learning applications in endocrine disorders caused by either precocious onset of adult hormone or abnormal amount of hormone production. To give access to the broader audience, we start with a gentle introduction to deep learning and its most commonly used architectures, and then we focus on the research trends of deep learning applications in thyroid dysfunction classification and precocious puberty diagnosis. We highlight the strengths and weaknesses of various approaches and discuss potential solutions to different challenges. We also go through the practical considerations useful for choosing (and building) the deep learning model, as well as for understanding the thought process behind different decisions made by these models. Finally, we give concluding remarks and future directions.

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Section: Application Of Deep Learning In Endocrinologymentioning

confidence: 99%

An overview of deep learning applications in precocious puberty and thyroid dysfunction

2022

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“…Then, the Mo-bileNetV3 model was implemented for feature extraction and age detection as the head and backbone of multi-layer perceptron (MLP) prediction. The model proposed in [3] covers three main stages: (a) segmentation using Otsu's method, (b) feature learning based on Faster R-CNN, and (c) classification using the RNN algorithm. The method in [9] used a Mask R-CNN network for segmentation by first drawing a bounding box around the hand image.…”

Section: Related Workmentioning

confidence: 99%

“…Pediatric endocrine problems and growth abnormalities are frequently diagnosed with BAA. Furthermore, a precise skeletal age assessment facilitates the estimation of height in adolescents, and diagnoses a number of diseases, including idiopathic dwarfism and early puberty [3,4]. The BAA technique can help effectively in other similar research like calculating bone fracture risk through evaluation of cortical bone fracture resistance curves [1].…”

Section: Introductionmentioning

confidence: 99%

A Bone Age Assessment based on a hybrid Knowledge Distillation Paradigm using single ROI

Mardanpour

Sepahvand

Abdali-Mohammadi

et al. 2022

Preprint

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Bone age assessment (BAA) has been widely used in clinical area from pediatric endocrine diagnosis to estimating height of children for adulthood. Advancements in technology has accelerated the evolution of BAA methodologies, one of which is deep learning algorithms, which overcome the drawbacks of conventional approaches. In spite of excellent effectiveness of deep neural networks in detection of the correct class for bone age, they have a significant degree of complexity due to the numerous parameters they employ for each ROI. Therefore, they are not suitable for implementation in edge devices with limited resources. In this paper, we propose a BAA method using a hybrid knowledge distillation (KD) paradigm in order to conquer this difficulty by mapping different ROIs into a single ROI. In this regard, the student receives knowledge from a teacher network that has been pre-trained on six ROIs including bones of five fingers and the wrist, transfers the knowledge of its final response layer and internal layers to the student. Then, six student models each of which is constructed based on just one of these ROIs, while receiving the information of the teacher model by matching the feature maps of intermediate layers and last output of teacher with those of student in distillation modules. Empirical results on Digital Hand Atlas (DHA) report that our student model trained on one ROI obtains 95% accuracy on 19 classes of bone age makes it appropriate for medical IoT deployment. Also, it has competitive performance compared to the other state-of-the-art BAA studies, and performance analysis of our KD with three base KD models indicates superiority of our introduced method.

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“…This unsupervised method can realize target detection based on differentiable feature clustering, which reduces the requirement of manual annotation but cannot achieve the scoring of specific reference bones. By improving Faster R-CNN, Deshmukh et al [21] proposed a TW3-based automatic bone age assessment (BAA) model for children's bone age recognition. The BAA model includes image segmentation, feature extraction, and classification prediction.…”

Section: Related Workmentioning

confidence: 99%

Hamate classification method based on feature‐enhanced residual network and probabilistic joint judgment

Ding

Zong

Ding

et al. 2022

IET Image Processing

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Accurate analysis of the maturity grade of the reference bone in the wrist is critical for bone age assessment. The main difference between the maturity grades of the reference bone in the wrist X‐ray image is the difference in the texture and morphological characteristics of the bone, and the difference in the characteristics between adjacent levels is small, which brings great challenges in bone age assessment. The hamate (the wrist bone in line with the 4th and 5th fingers) is one of the most important reference bones in the standards of skeletal maturity of the hand and wrist for the Chinese (CHN) method. Aiming at the problem of hamate maturity level evaluation, a method of hamate maturity level classification based on feature enhanced residual network and probability joint judgment is proposed. In this method, we propose the enhanced characteristic residual network (ECR‐Net) is proposed to enhance the feature extraction capability of the network and improve the loss function to reduce the impact of cross‐grade errors on the accuracy of bone age assessment. On this basis, multiple convolutional neural networks to make joint probabilistic judgments are relied on to obtain the final hamate maturity grade. The proposed ECR‐Net achieves a macro accuracy of 96.92% on the hamate maturity evaluation, and there are no errors across two levels. Also, this approach achieves a macro accuracy of 97.15% when the probabilistic joint judgment method is employed while avoiding errors that span more than two levels. The network model and method proposed in this paper have good accuracy and practicability for the automatic identification of the hamate maturity level, which is of great significance for the accurate assessment of bone age.

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Faster Region-Convolutional Neural network oriented feature learning with optimal trained Recurrent Neural Network for bone age assessment for pediatrics

Cited by 16 publications

References 32 publications

An overview of deep learning applications in precocious puberty and thyroid dysfunction

An overview of deep learning applications in precocious puberty and thyroid dysfunction

A Bone Age Assessment based on a hybrid Knowledge Distillation Paradigm using single ROI

Hamate classification method based on feature‐enhanced residual network and probabilistic joint judgment

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