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In this report we propose a classification technique for skin lesion imagesas a part of our submission for ISIC 2018 Challenge in Skin Lesion AnalysisTowards Melanoma Detection. Our data was extracted from the ISIC 2018: SkinLesion Analysis Towards Melanoma Detection grand challenge datasets. Thefeatures are extracted through a Convolutional Neural Network, in our caseResNet50 and then using these features we train a DeepForest, having cascadinglayers, to classify our skin lesion images. We know that Convolutional NeuralNetworks are a state-of-the-art technique in representation learning forimages, with the convolutional filters learning to detect features from imagesthrough backpropagation. These features are then usually fed to a classifierlike a softmax layer or other such classifiers for classification tasks. In ourcase we do not use the traditional backpropagation method and train a softmaxlayer for classification. Instead, we use Deep Forest, a novel decision treeensemble approach with performance highly competitive to deep neural networksin a broad range of tasks. Thus we use a ResNet50 to extract the features fromskin lesion images and then use the Deep Forest to classify these images. Thismethod has been used because Deep Forest has been found to be hugely efficientin areas where there are only small-scale training data available. Also as theDeep Forest network decides its complexity by itself, it also caters to theproblem of dataset imbalance we faced in this problem.

Disease Classification within Dermascopic Images Using features extracted by ResNet50 and classification through Deep Forest