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Hierarchical Classification of Skin Cancer Images.pptx

  1. Hierarchical Classification of Skin Cancer Images
  2. • Detection of Skin Cancer is usually done by visual diagnosis that comprises of an initial screening of skin lesions by analysing features like colour, depth and texture of lesions by medical professionals, potentially followed by dermoscopic evaluation, biopsies and histopathological examination. In this project we extract domain knowledge out of image data of the lesions, creating a hierarchy of classes of skin cancer by the use of classification using binary(pairwise) classifiers and applying a voting mechanism on the results
  3. • Previously only deep learning algorithms like CNN have been used for cancer image classification requiring very large number of images to train the model to be able to achieve desired accuracy of prediction. In this project we develop an algorithm using the power of binary classifiers and voting on predictions to be able to get more accuracy (76% for our model as compared to 22% of a pretrained CNN model for same image dataset!) hence making the requirement of large number of image dataset dispensable.
  4. • Initially, lesions are classified into various diagnoses with the help of convolutional neural networks (CNNs) for automated classification of lesion images with fine grained variability. Input to this CNN model are diagnosis labels and images. The output of the fully connected layer from CNN is then used to extract features and build a binary hierarchical classifier (bottom up approach) based on similarity between classes by using voting mechanism on pairwise classifiers and hence the taxonomy that is otherwise known only to domain experts and dermatologists, is now derived from data. This fast and scalable method can further be deployed on mobile applications hence bringing primary healthcare to a broader range of use
  5. • The Inception V3 model architecture that was pre-trained using 1.28 million images for 1000 categories from the ImageNet Large Scale Visual Recognition Challenge was used in this project and trained on Skin Cancer image dataset using transfer learning. The output of this model from the last fully connected layer after adding few added layers, is then used as input to the algorithm of binary classification and voting
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