# Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network

arXiv: https://arxiv.org/abs/1703.00577 · HTML: https://mlsanity.com/paper/1703.00577/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2017.

## Built on by (most cited first, 21 of 21)

- [AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks](https://mlsanity.com/paper/2305.09666/) (2023)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity](https://mlsanity.com/paper/2102.04635/) (2021)
- [Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification](https://mlsanity.com/paper/2109.09818/) (2021)
- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack](https://mlsanity.com/paper/2111.14564/) (2021)
- [Knowledge Distillation approach towards Melanoma Detection](https://mlsanity.com/paper/2210.08086/) (2022)
- [Image Complexity Guided Network Compression for Biomedical Image Segmentation](https://mlsanity.com/paper/2107.02927/) (2021)
- [Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning](https://mlsanity.com/paper/2303.14615/) (2023)
- [Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism](https://mlsanity.com/paper/2111.08708/) (2021)
- [Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs](https://mlsanity.com/paper/2202.03583/) (2022)
- [Deep Neural Networks Fused with Textures for Image Classification](https://mlsanity.com/paper/2308.01813/) (2023)
- [Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets](https://mlsanity.com/paper/2103.12068/) (2021)
- [Skin lesion segmentation and classification using deep learning and handcrafted features](https://mlsanity.com/paper/2112.10307/) (2021)
- [Medical Image Analysis using Deep Relational Learning](https://mlsanity.com/paper/2303.16099/) (2023)
- [Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review](https://mlsanity.com/paper/2309.00265/) (2023)
- [Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis](https://mlsanity.com/paper/2311.01009/) (2023)
- [Transformer-CNN Fused Architecture for Enhanced Skin Lesion Segmentation](https://mlsanity.com/paper/2401.05481/) (2024)
- [Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation](https://mlsanity.com/paper/2402.02649/) (2024)
- [Melanoma Classification Through Deep Ensemble Learning and Explainable AI](https://mlsanity.com/paper/2511.00246/) (2025)
- [MoSSGate: Memory-Modulated State-Space Gating for Skin Lesion Segmentation](https://mlsanity.com/paper/2609.20181/) (2026)

## Abstract (arXiv)

Skin lesion is a severe disease in world-wide extent. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons, e.g. low contrast between lesions and skin, visual similarity between melanoma and non-melanoma lesions, etc. Hence, reliable automatic detection of skin tumors is very useful to increase the accuracy and efficiency of pathologists. International Skin Imaging Collaboration (ISIC) is a challenge focusing on the automatic analysis of skin lesion. In this paper, we proposed two deep learning methods to address all the three tasks announced in ISIC 2017, i.e. lesion segmentation (task 1), lesion dermoscopic feature extraction (task 2) and lesion classification (task 3). A deep learning framework consisting of two fully-convolutional residual networks (FCRN) is proposed to simultaneously produce the segmentation result and the coarse classification result. A lesion index calculation unit (LICU) is developed to refine the coarse classification results by calculating the distance heat-map. A straight-forward CNN is proposed for the dermoscopic feature extraction task. To our best knowledges, we are not aware of any previous work proposed for this task. The proposed deep learning frameworks were evaluated on the ISIC 2017 testing set. Experimental results show the promising accuracies of our frameworks, i.e. 0.718 for task 1, 0.833 for task 2 and 0.823 for task 3 were achieved.