Arm fracture detection in X-rays based on improved deep convolutional neural network
23 Oct 2020 | Detection, Fracture
## ** Goal **
This paper’s goal is to propose a novel deep learning method to detect arm fracture in X-rays.
## ** Contribution **
1. New backbone network based on feature pyramid architecture
2. Image preprocessing procedure
**3. Receptive field adjustment with anchor scale reduction and tiny ROIs expansion
## ** Method **
** Backbone network**
FPN + Fast R-CNN +RPN with <u?Gaussian non-local attention module (refine integrated features)</u>
Integration of features (Novel method)
** Preprocessing **
Noise removal –> Morphological method
Brightening –> Cumulative distribution function
** Anchor scales reduction **
{P2; P3; P4; P5; P6} : {512; 256; 128; 64; 32} {256; 128; 64; 32; 16}
Guarantees more foreground RoIs for RPN training because GT bounding boxes are too small
** Expanding receptive field to fine tiny fracture **
Adding pixels to width and height for small ROIs (length adjustment)
Extract useful info from tiny ROIs
## ** Goal **
This paper’s goal is to propose a novel deep learning method to detect arm fracture in X-rays.
## ** Contribution **
1. New backbone network based on feature pyramid architecture
2. Image preprocessing procedure
**3. Receptive field adjustment with anchor scale reduction and tiny ROIs expansion
## ** Method **
** Backbone network**
FPN + Fast R-CNN +RPN with <u?Gaussian non-local attention module (refine integrated features)</u> Integration of features (Novel method)
** Preprocessing **
Noise removal –> Morphological method Brightening –> Cumulative distribution function
** Anchor scales reduction **
{P2; P3; P4; P5; P6} : {512; 256; 128; 64; 32} {256; 128; 64; 32; 16} Guarantees more foreground RoIs for RPN training because GT bounding boxes are too small
** Expanding receptive field to fine tiny fracture **
Adding pixels to width and height for small ROIs (length adjustment) Extract useful info from tiny ROIs
Comments