Diagnostic accuracy of content-based dermatoscopic image retrieval with deep classification features.
Br J Dermatol 2018;
181:155-165. [PMID:
30207594 PMCID:
PMC7379719 DOI:
10.1111/bjd.17189]
[Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2018] [Indexed: 12/20/2022]
Abstract
BACKGROUND
Automated classification of medical images through neural networks can reach high accuracy rates but lacks interpretability.
OBJECTIVES
To compare the diagnostic accuracy obtained by using content-based image retrieval (CBIR) to retrieve visually similar dermatoscopic images with corresponding disease labels against predictions made by a neural network.
METHODS
A neural network was trained to predict disease classes on dermatoscopic images from three retrospectively collected image datasets containing 888, 2750 and 16 691 images, respectively. Diagnosis predictions were made based on the most commonly occurring diagnosis in visually similar images, or based on the top-1 class prediction of the softmax output from the network. Outcome measures were area under the receiver operating characteristic curve (AUC) for predicting a malignant lesion, multiclass-accuracy and mean average precision (mAP), measured on unseen test images of the corresponding dataset.
RESULTS
In all three datasets the skin cancer predictions from CBIR (evaluating the 16 most similar images) showed AUC values similar to softmax predictions (0·842, 0·806 and 0·852 vs. 0·830, 0·810 and 0·847, respectively; P > 0·99 for all). Similarly, the multiclass-accuracy of CBIR was comparable with softmax predictions. Compared with softmax predictions, networks trained for detecting only three classes performed better on a dataset with eight classes when using CBIR (mAP 0·184 vs. 0·368 and 0·198 vs. 0·403, respectively).
CONCLUSIONS
Presenting visually similar images based on features from a neural network shows comparable accuracy with the softmax probability-based diagnoses of convolutional neural networks. CBIR may be more helpful than a softmax classifier in improving diagnostic accuracy of clinicians in a routine clinical setting.
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