Classification of Medical Images using Deep and Handcrafted Visual Feature-based Algorithm

Abstract

Though the medical images contain a lot of information, there is a problem in retrieving that information and make it useful for further diagnosis. Information from these medical images can be utilized effectively by implementing classification and retrieval. Features of the image are the most important factors for the image classification. Different kinds of handcrafted features are available which include single descriptors for colour, texture, and shape and combined descriptors. Different feature extraction algorithms like LBP and BOF are used to extract these features. We also have many deep learning techniques that extract deep learned features and are widely acknowledged as a powerful tool for image classification. But due to lack of not large enough dataset, over-fitting may occur. To address these problems, a combined deep and handcrafted visual feature-based algorithm is implemented in this work.