# Skin Lesion Classification Using Hybrid Deep Neural Networks

arXiv: https://arxiv.org/abs/1702.08434 · HTML: https://mlsanity.com/paper/1702.08434/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2017.

## Built on by (most cited first, 14 of 14)

- [A survey, review, and future trends of skin lesion segmentation and classification](https://mlsanity.com/paper/2208.12232/) (2022)
- [LesionAid: Vision Transformers-based Skin Lesion Generation and Classification](https://mlsanity.com/paper/2302.01104/) (2023)
- [A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information](https://mlsanity.com/paper/2104.14353/) (2021)
- [The Forward-Forward Algorithm as a feature extractor for skin lesion classification: A preliminary study](https://mlsanity.com/paper/2307.00617/) (2023)
- [Dermoscopic Dark Corner Artifacts Removal: Friend or Foe?](https://mlsanity.com/paper/2306.13446/) (2023)
- [Histopathological Image Classification and Vulnerability Analysis using Federated Learning](https://mlsanity.com/paper/2310.07380/) (2023)
- [Self-supervised learning for skin cancer diagnosis with limited training data](https://mlsanity.com/paper/2401.00692/) (2024)
- [Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets](https://mlsanity.com/paper/2103.12068/) (2021)
- [Learning Robust Convolutional Neural Networks with Relevant Feature Focusing via Explanations](https://mlsanity.com/paper/2202.04237/) (2022)
- [A Comprehensive Evaluation Study on Risk Level Classification of Melanoma by Computer Vision on ISIC 2016-2020 Datasets](https://mlsanity.com/paper/2302.09528/) (2023)
- [Deep learning and classical computer vision techniques in medical image analysis: Case studies on brain MRI tissue segmentation, lung CT COPD registration, and skin lesion classification](https://mlsanity.com/paper/2502.19258/) (2025)
- [Detecting Dataset Bias in Medical AI: A Generalized and Modality-Agnostic Auditing Framework](https://mlsanity.com/paper/2503.09969/) (2025)
- [Fusion of Foundation and Vision Transformer Model Features for Dermatoscopic Image Classification](https://mlsanity.com/paper/2505.16338/) (2025)
- [Privileged Lesion-Context Relational Distillation for Mask-Free Skin Lesion Classification](https://mlsanity.com/paper/2607.18773/) (2026)

## Abstract (arXiv)

Skin cancer is one of the major types of cancers with an increasing incidence over the past decades. Accurately diagnosing skin lesions to discriminate between benign and malignant skin lesions is crucial to ensure appropriate patient treatment. While there are many computerised methods for skin lesion classification, convolutional neural networks (CNNs) have been shown to be superior over classical methods. In this work, we propose a fully automatic computerised method for skin lesion classification which employs optimised deep features from a number of well-established CNNs and from different abstraction levels. We use three pre-trained deep models, namely AlexNet, VGG16 and ResNet-18, as deep feature generators. The extracted features then are used to train support vector machine classifiers. In the final stage, the classifier outputs are fused to obtain a classification. Evaluated on the 150 validation images from the ISIC 2017 classification challenge, the proposed method is shown to achieve very good classification performance, yielding an area under receiver operating characteristic curve of 83.83% for melanoma classification and of 97.55% for seborrheic keratosis classification.