Abstract:
A well-established paradigm exists for fracture identification in medical imaging. Computer-
assisted diagnostic systems (CAD) are used by many physicians and other health care providers
nowadays to help them detect a wide range of ailments more accurately by analyzing medical
pictures. An osteoarthritis, pressure, or trauma are the usual causes of fractures in bones. Bone,
which supports the entire body, is also a hard material. The bone breakage is therefore thought to
be the main issue of the past twelve months. The aim of the study is to determine if both of these
bones are both shattered and to identify the kind of injury using an x-ray picture. Deep learning-
based algorithms were used in this experiment along with two other class types: Fracture and
Normal. In order to predict and recognize X-ray images and categorize bone fractures, five models
are used: MobileNetV2, InceptionV3, VGG16, VGG19, and InceptionResNetV2. Lastly, two
distinct evaluations of efficiency are used to evaluate the technique's outcomes. The first accuracy
set, rating performance for fractures as well as normal situations, employs four potential the
results: TP, TN, FP, and FN. With its 94.23% accuracy rate, the InceptionResNetV2 technique
makes it possible for my proposed method to autonomously recognize femur and tibia fractures in
bones. Ultimately, a web prototype
