Disease Classification within Dermascopic Images Using features extracted by ResNet50 and classification through Deep Forest
This work addresses a domain-specific problem in medical imaging for melanoma detection, but it is incremental as it combines existing methods (ResNet50 and Deep Forest) without introducing new paradigms.
The authors tackled skin lesion classification for melanoma detection by using ResNet50 for feature extraction and Deep Forest for classification, achieving competitive performance on the ISIC 2018 dataset, though no specific numerical results are provided.
In this report we propose a classification technique for skin lesion images as a part of our submission for ISIC 2018 Challenge in Skin Lesion Analysis Towards Melanoma Detection. Our data was extracted from the ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection grand challenge datasets. The features are extracted through a Convolutional Neural Network, in our case ResNet50 and then using these features we train a DeepForest, having cascading layers, to classify our skin lesion images. We know that Convolutional Neural Networks are a state-of-the-art technique in representation learning for images, with the convolutional filters learning to detect features from images through backpropagation. These features are then usually fed to a classifier like a softmax layer or other such classifiers for classification tasks. In our case we do not use the traditional backpropagation method and train a softmax layer for classification. Instead, we use Deep Forest, a novel decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. Thus we use a ResNet50 to extract the features from skin lesion images and then use the Deep Forest to classify these images. This method has been used because Deep Forest has been found to be hugely efficient in areas where there are only small-scale training data available. Also as the Deep Forest network decides its complexity by itself, it also caters to the problem of dataset imbalance we faced in this problem.