Induced Acoustic Resonance for Noninvasive Bone Fracture Detection Using Digital Signal Processing and Machine Learning

Boger, Isaac; Chakalasiya, Jay; Christofferson, Kenneth; Wang, Yuntao; Raiti, John

doi:10.1109/ghtc46280.2020.9342913

Cited by 1 publication

(1 citation statement)

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“…Zhang et al [12] introduced a new window loss function for fracture detection and localization, enhancing localization accuracy. Boger et al [13] explored induced acoustic resonance for noninvasive fracture detection, introducing a unique nonimaging technique.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning Empowered Bone Fracture Detection

Baig,

Ul Haq,

Akram

et al. 2024

Decis. Mak. Adv.

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Detection of bone fractures using modern technology has significant implications in medical analysis and artificial intelligence. This importance is especially pronounced in the realm of deep learning. Deep learning techniques find extensive application in the field of medicine and disease classification. The early identification of bone fractures is crucial for efficient treatment planning and patient care. Our research proposes a transfer learning-based model for predicting bone fractures using a dataset of bone X-ray images. These images will be classified into two categories: normal and bone fracture, based on extracted features. Our proposed model, the Bone Fracture Detection Transfer Learning Algorithm (BFDTLA), achieved an average accuracy of 97% on the dataset. The BFDTLA model demonstrated superior performance when compared to previous quantitative and qualitative research studies. This research focuses on the early detection of bone fractures using transfer learning algorithms, emphasizing the significance of accurate and timely diagnosis.

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Section: Related Workmentioning

confidence: 99%