# Skin Cancer Detection utilizing Deep Learning: Classification of Skin Lesion Images using a Vision Transformer

arXiv: https://arxiv.org/abs/2407.18554 · HTML: https://mlsanity.com/paper/2407.18554/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 49 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Overfitting in adversarially robust deep learning](https://mlsanity.com/paper/2002.11569/) (2020)
- [TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment](https://mlsanity.com/paper/2406.01638/) (2024)
- [Spatial-Temporal Large Language Model for Traffic Prediction](https://mlsanity.com/paper/2401.10134/) (2024)
- [The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling](https://mlsanity.com/paper/2001.00570/) (2020)
- [TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems](https://mlsanity.com/paper/2311.11315/) (2023)
- [Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach](https://mlsanity.com/paper/2311.13884/) (2023)
- [Jointly Contrastive Representation Learning on Road Network and Trajectory](https://mlsanity.com/paper/2209.06389/) (2022)
- [Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data](https://mlsanity.com/paper/1909.09148/) (2019)
- [Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction](https://mlsanity.com/paper/1912.05693/) (2019)
- [KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategy](https://mlsanity.com/paper/2311.02565/) (2023)
- [Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile](https://mlsanity.com/paper/2004.11022/) (2020)
- [Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping](https://mlsanity.com/paper/2306.06994/) (2023)
- [DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge](https://mlsanity.com/paper/2312.14532/) (2023)
- [Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning](https://mlsanity.com/paper/2401.12681/) (2024)
- [Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling](https://mlsanity.com/paper/2404.04403/) (2024)
- [Decision Support System for Detection and Classification of Skin Cancer using CNN](https://mlsanity.com/paper/1912.03798/) (2019)
- [Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering](https://mlsanity.com/paper/2306.13794/) (2023)
- [Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering](https://mlsanity.com/paper/2310.20224/) (2023)
- [A Critical Perceptual Pre-trained Model for Complex Trajectory Recovery](https://mlsanity.com/paper/2311.02631/) (2023)
- [VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network](https://mlsanity.com/paper/2312.06428/) (2023)
- [Relation-Aware Distribution Representation Network for Person Clustering with Multiple Modalities](https://mlsanity.com/paper/2308.00588/) (2023)

## Built on by (most cited first, 3 of 3)

- [Optimization of Transformer heart disease prediction model based on particle swarm optimization algorithm](https://mlsanity.com/paper/2412.02801/) (2024)
- [TRUST: Test-time Resource Utilization for Superior Trustworthiness](https://mlsanity.com/paper/2506.06048/) (2025)
- [Residual GRU+MHSA: A Lightweight Hybrid Recurrent Attention Model for Cardiovascular Disease Detection](https://mlsanity.com/paper/2512.14563/) (2025)

## Abstract (arXiv)

Skin cancer detection still represents a major challenge in healthcare. Common detection methods can be lengthy and require human assistance which falls short in many countries. Previous research demonstrates how convolutional neural networks (CNNs) can help effectively through both automation and an accuracy that is comparable to the human level. However, despite the progress in previous decades, the precision is still limited, leading to substantial misclassifications that have a serious impact on people's health. Hence, we employ a Vision Transformer (ViT) that has been developed in recent years based on the idea of a self-attention mechanism, specifically two configurations of a pre-trained ViT. We generally find superior metrics for classifying skin lesions after comparing them to base models such as decision tree classifier and k-nearest neighbor (KNN) classifier, as well as to CNNs and less complex ViTs. In particular, we attach greater importance to the performance of melanoma, which is the most lethal type of skin cancer. The ViT-L32 model achieves an accuracy of 91.57% and a melanoma recall of 58.54%, while ViT-L16 achieves an accuracy of 92.79% and a melanoma recall of 56.10%. This offers a potential tool for faster and more accurate diagnoses and an overall improvement for the healthcare sector.