Osteoporosis Detection Using Deep Learning: A Computer-Aided Diagnosis System

Abstract

Osteoporosis represents a significant skeletal disorder marked by diminished bone mineral density (BMD) and compromised micro architectural integrity, substantially elevating fracture susceptibility, especially among aging populations. This investigation introduces an artificial intelligence-based diagnostic framework that leverages deep neural networks to evaluate radiographic and DEXA imaging data. The architecture uses a convolutional neural network (CNN) framework learnt on curated medical image databases to distinguish normal and bone pattern of a diseased (osteoporotic) bone. The given technology solution can be characterized by a distributed web architecture that will integrate Flask-based analytical services, an interactive interface designed with React.js, and enable receiving instant diagnostic analysis. This study introduces a novel hybrid deep learning model for osteoporosis detection, outperforming existing methods in accuracy and additionally offering treatment and precaution recommendations and as a decision aids system meant to help the medical practitioners, this system provides an effective low-cost and scalable solution of initial osteoporosis assessments.

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