D3FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging

Providing accurate and speedy diagnosis and, in turn, treatment, automated medical image analysis plays a significant role in survival rate improvement. Inherent different kinds of uncertainties and complexities prove machine learning-based, particularly dictionary-learning-based classification approaches, very promisingly. This work concerns class-specific fuzzy discriminative dictionary learning using deep features on the continuum of our machine-learning-based medical image classifiers’ evolution path. In D3FC, a deep autoencoder generates a more relevant, representative, and compact features set. The distinctive-hidden information and inherent complexity and uncertainty of medical images are addressed using fuzzy-discriminative terms in the optimization function, simultaneously improving the inter-class-representation distance and intra-class-representation similarity. A comprehensive set of experiments on cancer tumor images from three different databases shows the outperformance of D3FC over related state-of-the-art competitions in accuracy, sensitivity, specificity, precision, convergence speed, and noise resilience. The meaningfulness of the experiments’ results is statistically verified.