Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification
This work addresses skin lesion diagnosis for medical imaging, but it is incremental as it applies existing methods to a specific dataset.
The authors tackled automated disease classification in dermoscopic images by using an ensemble of VGG16 and GoogLeNet with transfer learning, preprocessing, and image augmentation, achieving results evaluated on the ISIC 2018 Task 3 benchmark.
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.