# Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms

arXiv: https://arxiv.org/abs/1810.10348 · HTML: https://mlsanity.com/paper/1810.10348/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2018.

## Built on by (most cited first, 11 of 11)

- [Soft-Attention Improves Skin Cancer Classification Performance](https://mlsanity.com/paper/2105.03358/) (2021)
- [Semi-supervised learning for medical image classification using imbalanced training data](https://mlsanity.com/paper/2108.08956/) (2021)
- [Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-based Non-invasive Digital System](https://mlsanity.com/paper/2401.04746/) (2024)
- [Two-stage Deep Denoising with Self-guided Noise Attention for Multimodal Medical Images](https://mlsanity.com/paper/2503.06827/) (2025)
- [Optimizing Neuro-Fuzzy and Colonial Competition Algorithms for Skin Cancer Diagnosis in Dermatoscopic Images](https://mlsanity.com/paper/2505.08886/) (2025)
- [Text recognition on images using pre-trained CNN](https://mlsanity.com/paper/2302.05105/) (2023)
- [Deep Perceptual Enhancement for Medical Image Analysis](https://mlsanity.com/paper/2503.08027/) (2025)
- [Degradation-Aware All-in-One Image Restoration via Latent Prior Encoding](https://mlsanity.com/paper/2509.17792/) (2025)
- [LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection](https://mlsanity.com/paper/2109.05493/) (2021)
- [DenseNet approach to segmentation and classification of dermatoscopic skin lesions images](https://mlsanity.com/paper/2110.04632/) (2021)
- [Transformation Invariant Cancerous Tissue Classification Using Spatially Transformed DenseNet](https://mlsanity.com/paper/2204.11066/) (2022)

## Abstract (arXiv)

In this paper, the effectiveness and capability of convolutional neural networks have been studied in the classification of 8 skin diseases. Different pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152, Inception v3, InceptionResNet v2) were used and applied on 10135 dermoscopy skin images in total (HAM10000: 10015, PH2: 120). The utilized dataset includes 8 diagnostic categories - melanoma, melanocytic nevi, basal cell carcinoma, benign keratosis, actinic keratosis and intraepithelial carcinoma, dermatofibroma, vascular lesions, and atypical nevi. The aim is to compare the ability of deep learning with the performance of highly trained dermatologists. Overall, the mean results show that all deep learning models outperformed dermatologists (at least 11%). The best ROC AUC values for melanoma and basal cell carcinoma are 94.40% (ResNet 152) and 99.30% (DenseNet 201) versus 82.26% and 88.82% of dermatologists, respectively. Also, DenseNet 201 had the highest macro and micro averaged AUC values for overall classification (98.16%, 98.79%, respectively).