Adaptable Reconfigurable and Power-Efficient Robots for Health Monitoring

Dasi, Swathi; Malladhi, Nagarjuna; Tigadi, Arun; Majji, Sankararao; Patnala, Tulasi Radhika; Reddy, V. Prabhakar

doi:10.1109/icaect54875.2022.9808048

Cited by 2 publications

(1 citation statement)

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“…Some frequent groupings based on patterns are as follows: It has been noted that traditional image processing approaches have given way to a variety of machine learning models, and that today, Deep Learning models are routinely utilized for fracture diagnosis [13]. Many models based on machine learning and deep learning were created to diagnose fractures due to the growing demand for automated systems that can do so from X-Ray pictures, which would be of tremendous use to physicians [14]. These models' precision is proportional to the size of the data used to train them.…”

Section: Pattern-based Classificationmentioning

confidence: 99%

Analysis of Advanced Deep Learning Approaches for the Multiple Bone Fracture detection

Lydia¹,

Kumar

Rao³

2023

Preprint

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An immediate requirement in modern medicine is for computer-aided detection and diagnosis of bone fractures. Radiologists can save time and work more efficiently as a result. In the past, numerous different image processing methods were utilized to spot bone breaks. In the field of medical image processing, deep learning models in the form of specialized convolutional neural networks are currently in widespread usage. Its scope is broadened to include bone fracture detection in X-Ray images. The severity of injuries can be estimated by extracting information such as the presence of fractures, their locations, and the distances in length, width, and depth between the broken bones in an automated method. We present methods for identifying bone fractures in diagnostic imaging. The major objective is to use multiple deep learning models to detect these cracks. Patients' elbow, hand, and foot X-rays are separated into their own categories in the dataset. The proposed system analyzes or diagnoses the output using frame difference and a data analytics approach. All models have their feature extraction handled by the proposed method. Because it double-checks feature extractions, it rarely fails to detect a fracture. The proposed system is worn on the hand's wrist. This software tells users the state of their fractures once they answer a few simple questions and upload an X-ray. The image is subsequently processed by the application's built-in models, with all results (whether or not a fracture was identified) shown in the user interface. Python scripts are incorporated into the application's C#-based Dotnet framework. Better sensitivity and specificity are achieved, with an overall accuracy of 89%. Correct diagnosis is crucial since wrong diagnoses can have devastating effects on patients.

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Section: Pattern-based Classificationmentioning

confidence: 99%