Impact of quantization on various CNN architectures for bone fracture detection

The skeletal system, comprising bones, plays a crucial role in physical support and locomotion. Any deformation of bones, due to trauma or medical conditions like osteoporosis, can lead to a bone fracture. Bone fractures are painful conditions requiring immediate medical attention. Automating fracture detection can play a significant role in early diagnosis and intervention for bone fractures. Convolutional neural networks (CNN) have shown promising results in multiple medical imaging applications. There is a wide variety of convolutional neural network architectures. Due to their high computational and power requirements, they are not being adopted in resource-constrained environments. Quantization is a technique by which the size and power consumption a model are minimized with minimal loss of accuracy. It is performed by converting higher-precision data types to lower-precision data types. It enables the adoption of complex artificial intelligence models in resource-constrained environments such as mobile devices, edge devices, and IoT devices. The work focuses on finding the optimal convolutional neural network architecture and quantization method for bone fracture detection. For comparison, seven models were selected from different convolutional neural network architectures, namely, LeNet, AlexNet, VGG 19, Inception-V3, ResNet-152, MobileNetV2, and EfficientNet-B0. A baseline transformer, ViT-B/16, was also considered. Dynamic range quantization, float-16, and int-8 quantization were performed to analyze their impact on the diagnostic and deployment performances of the model. TOPSIS and Cohen's d analyses were performed. It was observed that lightweight models, such as LeNet and MobileNetV2, benefit greatly from quantization. This study serves as a practical guide for optimizing in a resource-constrained environment.