  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.
