Output image after successively applying the image padding and image layering enhancement techniques.

The radiographs were randomly chosen from the ImageCLEF 2009 Medical Annotation Task Training Set. Modified from [21] under a CC BY license, with permission from [RWTH Aachen], original copyright [2009].</p

Prediction performance of the Inception-v3 image classification model on the various image input types.

The highlighted accuracies are the best per classification scheme. Evaluation was calculated on the ImageCLEF 2009 Medical Annotation Task Test Set.</p

Best prediction performances for the applied classification models.

The classification scheme is (D) directional axis and contains 34 classes.</p

Examples of radiographs annotated with two classes from the B-scheme.

(A) shows three imagse belonging to class ‘443’ representing ‘Gastrointestinal system; Small intestine; Ileum’ and (B) displays three images belonging to class ‘512’ representing ‘Uropoietic system; Kidney; Renal pelvis’. All radiographs were randomly chosen from the ImageCLEF 2009 Medical Annotation Task Training Set. Republished from [21] under a CC BY license, with permission from [RWTH Aachen], original copyright [2009].</p

Best prediction performances for the applied classification models.

The classification scheme is (A) anatomical axis and contains 97 classes.</p

Example of two grayscale radiographs annotated with the 13-digit classification code.

Both images were randomly chosen from the ImageCLEF 2009 Medical Annotation Task Training Set. Republished from [21] under a CC BY license, with permission from [RWTH Aachen], original copyright [2009].</p

Best prediction performances for the applied classification models.

The classification scheme is (T) technical axis and contains 6 classes.</p

Examples of radiographs annotated with two classes from the D-scheme.

(A) shows three image belonging to class ‘125’ representing ‘Coronal; Anteroposterior; Upright’ and (B) displays three images belonging to class ‘228’ representing ‘Sagital; Lateral, left-right; Inclination’. All radiographs were randomly chosen from the ImageCLEF 2009 Medical Annotation Task Training Set. Republished from [21] under a CC BY license, with permission from [RWTH Aachen], original copyright [2009].</p

Prediction performance of the Inception-ResNet-v2 image classification model on the various image input types.

The highlighted accuracies are the best per classification scheme. Evaluation was calculated on the ImageCLEF 2009 Medical Annotation Task Test Set.</p

Medical image before and after applying the Non Local Means (NL-MEANS) preprocessing method.

The radiograph was randomly chosen from the ImageCLEF 2009 Medical Annotation Task Training Set. Republished from [21] under a CC BY license, with permission from [RWTH Aachen], original copyright [2009].</p