Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification

08/15/2018

∙

In this report, we are presenting our automated prediction system for disease classification within dermoscopic images. The proposed solution is based on deep learning, where we employed transfer learning strategy on VGG16 and GoogLeNet architectures. The key feature of our solution is preprocessing based primarily on image augmentation and colour normalization. The solution was evaluated on Task 3: Lesion Diagnosis of the ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection.

READ FULL TEXT

Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification

Skin Lesion Analysis Towards Melanoma Detection via End-to-end Deep Learning of Convolutional Neural Networks

Expert Opinion Elicitation for Assisting Deep Learning based Lyme Disease Classifier with Patient Data

Skin cancer reorganization and classification with deep neural network

Joint Liver Lesion Segmentation and Classification via Transfer Learning

Style transfer-based image synthesis as an efficient regularization technique in deep learning

A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images

Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting

Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification

Related Research

Skin Lesion Analysis Towards Melanoma Detection via End-to-end Deep Learning of Convolutional Neural Networks

Expert Opinion Elicitation for Assisting Deep Learning based Lyme Disease Classifier with Patient Data

Skin cancer reorganization and classification with deep neural network

Joint Liver Lesion Segmentation and Classification via Transfer Learning

Style transfer-based image synthesis as an efficient regularization technique in deep learning

A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images

Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting