Abstract:
Fracture detection in medical imaging is a well-established paradigm. These days, a lot of doctors
and other medical professionals utilize computer-aided diagnostic systems (CAD) to assist them
diagnose a variety of illnesses more correctly by evaluating medical images. Similarly, typical
explanations of bone fractures include trauma, pressure, and osteoarthritis. In addition, bone is a
hard substance that supports the entire body. Thus, the bone fracture is regarded as the major
problem of the last year. In CAD systems, computerized vision-based bone fracture recognition is
growing more and more important since it reduces physician workload by identifying instances
that are easy to address. This paper introduces many image processing techniques to identify
different forms of fractures in the lower leg bones, the femur and tibia. The purpose of the research
is to use an x-ray image to detect the kind of fracture and ascertain if the tibia and femur are both
broken. Various approaches and algorithms have been created to accurately detect and classify
images based on the presence or absence of fractures in different body parts. In this particular
experiment, two distinct class types—Fracture and Normal—as in addition to deep learning-based
models were employed. The five models: MobileNetV2, InceptionV3, VGG16, VGG19, and
InceptionResNetV2 are utilized to anticipate and identify X-ray pictures in order to classify bone
fractures. Finally, the technique's results are assessed using two different performance
assessments. Performance evaluation for fractures and normal circumstances is the initial accuracy
set, and it uses four possible outcomes: TP, TN, FP, and FN. Using these models, the accuracy of
each kind of fracture in mistake scenarios is analyzed next. My suggested method opens the door
for autonomous recognition of femur & tibia fractures in bones thanks to the InceptionResNetV2
approach, which has a 94.23% accuracy rate. In the end, the InceptionResNetV2 network is
employed for classification in order to recognize fracture in order to generate a web prototype
