Multi Locale Bone Fracture Radiographs and Localization
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Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh
Abstract
We introduce MLBFR, a varied radiographs dataset of human bone fractures. The
dataset contains 2,583 radiographs, among which 410 have 575 fracture points.
A radiologist manually labelled the dataset as ”fractured” and ”non-fractured”
with masks for the fracture locations. The dataset was verified and approved by
an expert medical officer to evaluate the radiologist’s performance further. To
precisely detect and localize the fracture areas, we experimented with several state-
of-the-art object detection models, YOLOv5, maskRCNN, efficientDet and more,
along with their ensemble. The trained models fell under two criteria, one being
the full dataset and the other being only the fractured radiographs. The trained
models managed to achieve a precision of 78.9% and 91.65% on combined and only
fractured radiographs, respectively. The model performances were comparable
to that of radiologists in detecting major abnormalities in the arm and shinbone
area. With falling slightly behind in detecting fractures in the hip, thigh, and finger
fractures. It is our belief that the task of improving this performance will be a good
challenge for future research. To further encourage advancement in this area, we
intend to make this dataset freely available in the future.
Description
Supervised by
Mr. Tareque Mohmud Chowdhury & Mr. Tasnim Ahmed,
Department of Computer Science and Engineering(CSE),
Islamic University of Technology (IUT)
Board Bazar, Gazipur-1704, Bangladesh.
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
Citation
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