Deep learning approach to detect malaria from microscopic images

Malaria is an infectious disease which is caused by plasmodium parasite. Several image processing and machine learning based techniques have been employed to diagnose malaria, using its spatial features extracted from microscopic images. In this work, a novel deep neural network model is introduced for identifying infected falciparum malaria parasite using transfer learning approach. This proposed transfer learning approach can be achieved by unifying existing Visual Geometry Group (VGG) network and Support Vector Machine (SVM). Implementation of this unification is carried out by using Train top layers and freeze out rest of the layers strategy. Here, the pre-trained VGG facilitates the role of expert learning model and SVM as domain specific learning model. Initial 'k' layers of pre-trained VGG are retained and (n-k) layers are replaced with SVM. To evaluate the proposed VGG-SVM model, a malaria digital corpus has been generated by acquiring blood smear images of infected and non-infected malaria patients and compared with state-of-the-art Convolutional Neural Network (CNN) models. Malaria digital corpus images were used to analyse the performance of VGG19-SVM, resulting in classification accuracy of 93.1% in identification of infected falciparum malaria. Unification of VGG19-SVM shows superiority over the existing CNN models in all performance indicators such as accuracy, sensitivity, specificity, precision and F-Score. The obtained result shows the potential of transfer learning in the field of medical image analysis, especially malaria diagnosis.