ICONIP 2024

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This repository contains the official code for the paper "Enhancing wrist abnormality detection with YOLO: Analysis of state-of-the-art single-stage detection models". We achieved SOTA fracture detection results on GRAZPEDWRI-DX dataset. Also contains code for end-to-end application.

Detection of cracks in the building foundation

This repository contains code the official code for the paper "Pediatric Wrist Fracture Detection in X-rays via YOLOv10 Algorithm and Dual Label Assignment System"

This repository contains the official code for the paper "Learning from the Few: Fine-grained Approach to Wrist Pathology Recognition on a Limited Dataset".

From a selection of data from the Roboflow file https://universe.roboflow.com/landy-aw2jb/fracture-ov5p1/dataset/1, which represents a reduced but homogeneous version of that file, a model is obtained based on yolov10 with that custom dataset to indicate fractures in x-rays.

Indicates the location of wrist fractures in x-rays through training with yolo v8 of roboflow images downloaded from https://www.kaggle.com/datasets/pkdarabi/bone-fracture-detection-computer-vision-project/code

# Fracture.v1i_Reduced_SSD From a selection of data from the Roboflow file https://universe.roboflow.com/landy-aw2jb/fracture-ov5p1/dataset/1, which represents a reduced but homogeneous version of that file, try to perform fracture detection using SSD. A version with VGG16 and another with only linear layers are presented

From dataset https://universe.roboflow.com/roboflow-100/bone-fracture-7fylg a model is obtained, based on yolov10, with that custom dataset, to indicate fractures in x-rays. The project uses 5 cascade models, if one does not detect fracture it is passed to another

From dataset https://universe.roboflow.com/roboflow-100/bone-fracture-7fylg a model is obtained, based on ML (SVR), with that custom dataset, to indicate fractures in x-rays.

This project uses PyTorch to classify bone fractures. As well as fine-tuning some famous CNN architectures (like VGG 19, MobileNetV3, RegNet,...), we designed our own architecture. Additionally, we used Transformer architectures (such as Vision Transformer and Swin Transformer). This dataset is Bone Fracture Multi-Region X-ray, available on Kaggle.

From a selection of data from the Roboflow file https://universe.roboflow.com/landy-aw2jb/fracture-ov5p1/dataset/1, which represents a reduced but homogeneous version of that file, a model is obtained using an adaptation of the project https://github.com/mahdi-darvish/YOLOv3-from-Scratch-Analaysis-and-Implementation instead any yolo model

This repository contains the implementation code of the paper "Small Data, Big Impact: A Multi-Locale Bone Fracture Detection on an Extremely Limited Dataset Via Crack-Informed YOLOv9 Variants"

Using deep learning to classify wrist fractures from GRAZPEDWRI-DX dataset. Pinpointing important regions using the XAI algorithm GradCAM.

Deep learning-based model for automated classification of cervical spine fractures with a remarkable 99.67% accuracy, surpassing radiologists' performance. Utilizes AlexNet and GoogleNet architectures for efficient and fast diagnosis in medical applications, enhancing clinical and research-based workflows.

Detection of fractures in images by obtaining the X and Y coordinates of the center of the fracture applying ML (SVR). It is applied to a selection of data from the Roboflow file https://universe.roboflow.com/landy-aw2jb/fracture-ov5p1/dataset/1 Compared to other tests using DL for the same set of data, much better precision and training time