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In recent years, advances in machine learning and deep learning have led to increases in the research and exploration of potential applications in dermatology [2-6]. These advancements have led to the production of systems that can diagnose skin conditions through image analysis. With the help of clinical and dermoscopic images for training, convolutional neural networks (CNNs) have been able to compete and even outperform experienced dermatologists when diagnosing and classifying skin cancer [7-11].

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Statistical analysis was performed using Python software (version 3.8.13; Python Software Foundation) and Stata statistical software (version SE 17; StataCorp). The primary outcome measures were sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) for the binary classification of lesions.

AIO had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. AIO is supported by an Australian Government Research Training Program Scholarship. 041b061a72