A Survey on Bone Fracture Identification Techniques using Quantitative and Learning Based Algorithms

Saranya, A.; Kottilingam, K.

doi:10.1109/icais50930.2021.9395817

Cited by 19 publications

(2 citation statements)

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“…However, pattern recognition is a challenging task because it is not easy to locate a rhythm from the noisy data. However, several algorithms are being applied in pattern recognition, which include clustering, machine learning, deep learning, multi-linear subspace learning, and deep learning [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Integrative Bioinformatics Approaches Indicate a Particular Pattern of Some SARS-CoV-2 and Non-SARS-CoV-2 Proteins

et al. 2022

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Pattern recognition plays a critical role in integrative bioinformatics to determine the structural patterns of proteins of viruses such as SARS-CoV-2. This study identifies the pattern of SARS-CoV-2 proteins to depict the structure–function relationships of the protein alphabets of SARS-CoV-2 and COVID-19. The assembly enumeration algorithm, Anisotropic Network Model, Gaussian Network Model, Markovian Stochastic Model, and image comparison protein-like alphabets were used. The distance score was the lowest with 22 for “I” and highest with 40 for “9”. For post-processing and decision, two protein alphabets “C” (PDB ID: 6XC3) and “S” (PDB ID: 7OYG) were evaluated to understand the structural, functional, and evolutionary relationships, and we found uniqueness in the functionality of proteins. Here, models were constructed using “SARS-CoV-2 proteins” (12 numbers) and “non-SARS-CoV-2 proteins” (14 numbers) to create two words, “SARS-CoV-2” and “COVID-19”. Similarly, we developed two slogans: “Vaccinate the world against COVID-19” and “Say no to SARS-CoV-2”, which were made with the proteins structure. It might generate vaccine-related interest to broad reader categories. Finally, the evolutionary process appears to enhance the protein structure smoothly to provide suitable functionality shaped by natural selection.

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Section: Introductionmentioning

confidence: 99%

Integrative Bioinformatics Approaches Indicate a Particular Pattern of Some SARS-CoV-2 and Non-SARS-CoV-2 Proteins

et al. 2022

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“…That leads to spoiling the entire life with negative infirmity. Various techniques of analysing bone diseases with the deformity metrics are discussed in (Saranya & Kottilingam, 2021). Now a day's medical treatments are equipped with artificial intelligence, which is deeply connected with iterated modules to recognize complicated medical cases.…”

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confidence: 99%

Multi‐scaled feature fusion enabled convolutional neural network for predicting fibrous dysplasia bone disorder

Arirangan

Kottursamy

2021

Expert Systems

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Background: Convolutional Neural Network (CNN) has exciting advantages in the processing of medical images. It produces the denoised and segmented results of images. When an image has orientation and scaling issues over the bundle that is not correctly recognized by the classifier. Methods: To overcome these issues, we propose Extreme Deep Learning on Feature Fusion Convolutional Neural Network (EDL-FFCNN) for classification. Feature fusion workflow enhances the ability to learn the feature from the inconsistent behaviour. And, to learn the texture, colour, and intensities of an image. The initial step of the FFCNN framework is to reconstruct the images using filtered back projection. It reduces the noise in edges and cross-sectional areas. Contrast Limited Adaptive Histogram Equalization and Local Binary Pattern is applied to an image to adjust and extract the intensity and textural features of an image. The proposed FFCNN is a decision-based approach that consists of two persistent classifiers. One, the convolutional features are combined with manually extracted multi-scaled features. Subsequently, the normalization techniques are built-in every layer to adjust the input dimensions. It also helps to reduce the training time. As an outcome of Image abnormalities is considered as knowledge transfer to the CNN. Results: The fused features on the CNN classifier produce the abnormality prediction results. Second, the Inception V2 CNN classifier framework classifies the bone images with extracted features. With these two comparative-classification results, the major counting has been taken as the decision for final abnormality prediction.The proposed model gives improved accuracy of 95% for the elbow, 93% for the forearm, 92% for the shoulder, 92% for the wrist, 93% for the finger, 92% for humerus images.

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High-Speed Antenna Selection for Underwater Cognitive Radio Wireless Sensor Networks

Ganesh

Rajaprakash

2022

Sustainable Communication Networks and Application

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A Survey on Bone Fracture Identification Techniques using Quantitative and Learning Based Algorithms

Cited by 19 publications

References 50 publications

Integrative Bioinformatics Approaches Indicate a Particular Pattern of Some SARS-CoV-2 and Non-SARS-CoV-2 Proteins

Integrative Bioinformatics Approaches Indicate a Particular Pattern of Some SARS-CoV-2 and Non-SARS-CoV-2 Proteins

Multi‐scaled feature fusion enabled convolutional neural network for predicting fibrous dysplasia bone disorder

High-Speed Antenna Selection for Underwater Cognitive Radio Wireless Sensor Networks

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