Bone Fracture Detection and Classification Using Deep Learning Techniques

2. Detection And Classification Of Bone Fracture

In the field of orthopaedics, the absence of accessible and advanced tools for data-driven decision-making poses a significant challenge in addressing the intricate issues associated with bone fracture detection. Healthcare institutions dedicated to orthopaedic diagnostics face dual challenges of limited economic resources and a noticeable deficit in expertise related to advanced technologies such as coding, data analysis, and Machine Learning.

Traditional approaches to bone fracture detection often lack the precision required for proactive diagnosis and timely intervention. The reliance on manual interpretation of medical images can be time-consuming and subjective, leading to potential delays in treatment. The absence of predictive models for automated bone fracture detection further hinders the ability of healthcare professionals to optimize resource allocation and formulate targeted treatment plans.

Moreover, bone fractures, being multifaceted medical phenomena, demand a comprehensive understanding that extends beyond the conventional diagnostic methods. The dearth of accessible technology for orthopaedic institutions exacerbates these challenges, limiting their capacity to harness the potential of data-driven solutions.

The problem is twofold: the need for cost-effective, accessible technology tailored to orthopaedic diagnostics, and the necessity for predictive models that can enhance the accuracy and speed of bone fracture detection. This model seeks to bridge these gaps by leveraging Data Analysis and Machine to create a predictive model that enhances our ability to detect and classify bone fractures, empowering healthcare using a deep learning approach.