In Computer-Aided Diagnosis (CAD) systems, major role is played by classification of medical images. Conventional methods uses texture features, color and shape information in a combined manner for classification. These methods are problem specific and in medical images, they have shown their complementary, which makes the systems inability to make high-level problem domain concepts representation and they are having worst model generalization ability. In recent days, because of its admirable performance in different fields, great attention is gained by convolutional neural networks (CNN). However, complete training of a novel deep CNN model is concentrated in recent works to target issues with restricted data and time consuming issues. Various investigations are done for rectifying those drawbacks of existing techniques.

Feature Fusion, Multi-dimensional Features, Convolutional Neural Networks (CNN), Discriminative Feature Score, Medical Image Classification.