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Curated Dataset from GRAZPEDWRI-DX

The dataset used in this paper is a curated subset of GRAZPEDWRI‑DX, intended for fine‑grained recognition of wrist pathologies. It includes image data for training, validation, and testing sets.

Updated 8/24/2024
github

Description

Dataset Overview

Data Source

The dataset is carefully selected from GRAZPEDWRI‑DX for research on pediatric wrist pathology recognition. The curated dataset link is: Curated Dataset.

Dataset Structure

The dataset is split into training, validation, and test sets with the following structure:

train/
  0/
    0/0133_0306769778_07_WRI-R2_M015-1.png
    0133_0306769778_07_WRI-R2_M015-3.png
    ...
  1/
    0025_0483842914_01_WRI-L2_F000.png
    0053_1119833109_03_WRI-R1_F005.png
    ...
  ...
val/
  0/
    0133_0306769778_07_WRI-R2_M015-0.png
    0133_0306769778_07_WRI-R2_M015-2.png
    ...
  1/
    0042_0827512771_04_WRI-R2_M015.png
    0071_0680563744_02_WRI-R1_F009.png
    ...
  ...

test/
  0/
    0772_0547017117_03_WRI-R1_M017-0.png
    0772_0547017117_03_WRI-R1_M017-1.png
    ...
  1/
    0069_0502540283_01_WRI-L1_M013.png
    0078_1212376595_01_WRI-L1_M011.png
    ...
  ...

test2/
  0/
    0772_0547017117_03_WRI-R1_M017.png
    0834_0240036198_01_WRI-R1_M014.png
    ...
  1/
    0069_0502540283_01_WRI-L1_M013.png
    0115_0432451427_01_WRI-L2_M004.png
    ...

Pre‑trained Model

Weights for the refined fine‑grained visual recognition (FGVR) model are available at: Weights.

Evaluation Results

The method performs strongly on the limited test set, with detailed results as follows:

Comparison with Other Deep Neural Networks

ModelTest Accuracy (%)
EfficientNetV253.59
NFNet65.40
VGG1665.82
ViT70.25
DeiT370.89
RegNet72.36
DenseNet20173.42
MobileNetV276.37
CMAL76.58
RexNet10077.43
ResNet10177.43
IELT78.10
DenseNet12178.21
ResNest101e78.27
InceptionV478.69
MetaFormer78.90
ResNet5079.11
InceptionV379.54
EfficientNet_b079.96
YOLOv8x80.50
HERBS82.70
Our Approach (PIM for FGVR)84.38

LION Ensemble and FPN Tuning

ModelTest Set 1 Accuracy (%)Test Set 2 Accuracy (%)
PIM84.38...
PIM + LION85.44...

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Topics

Medical Image Recognition
Fine‑Grained Classification

Source

Organization: github

Created: 8/24/2024

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