# Meta-learning for skin cancer detection using Deep Learning Techniques

Source: [Meta-learning for skin cancer detection using Deep Learning Techniques](<https://www.sota2.com/research/paper/68fa69ca1e209131cea240d1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:08:38.583Z

Authors: Sara I. Garcia

Published: 2021-04-21T21:44:25.000Z

## Abstract

This study focuses on automatic skin cancer detection using a Meta-learning approach for dermoscopic images. The aim of this study is to explore the benefits of the generalization of the knowledge extracted from non-medical data in the classification performance of medical data and the impact of the distribution shift problem within limited data by using a simple class and distribution balancer algorithm. In this study, a small sample of a combined dataset from 3 different sources was used to fine-tune a ResNet model pre-trained on non-medical data. The results show an increase in performance on detecting melanoma, malignant (skin cancer), and benign moles with the prior knowledge obtained from images of everyday objects from the ImageNet dataset by 20 points. These findings suggest that features from non-medical images can be used towards the classification of skin moles and that the distribution of the data affects the performance of the model.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.10775>)

[PDF](<https://arxiv.org/pdf/2104.10775.pdf>)