An Explainable Deep Ensemble Framework for Bone Fracture Detection & Segmentation from X-ray Images with a Web-Based Interface

Key Contribution:
● Proposed a two-stage deep learning
approach for automatic detection and
segmentation of bone fractures based on
X-ray images.
● Combined and processed two Kaggle
X-ray datasets in order to get a balanced
dataset for bone fractures detection [7].
● Created a personalized segmented
dataset by doing manual annotation and
masks for fracture localization.
● Preprocessed and augmented the dataset
to increase the quality and
generalization of the deep learning
model [8].
● Used several deep learning models
(ResNet50, EfficientNetB3,
MobileNetV3, Xception, InceptionV3,
and DenseNet121) for detecting bone
fractures.
Designed a
Multi_Bone_Hybrid_DetModel based
on several most accurate detection
models to get better detection results.
● Used several U-Net-like segmentation
models and created a
Multi_Bone_Hybrid_SegModel to get
better fracture localization results.
● Added Grad-CAM for both detection
and segmentation models to improve
explainability and interpretation of the
model [9].
● Created a web-based application for
fracture detection, segmentation, and
visualization of Grad-CAM