Deep CNN frameworks comparison for malaria diagnosis
This work addresses malaria diagnosis for medical applications, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of diagnosing malaria by comparing AlexNet and VGGNet deep CNN frameworks for classifying healthy and infected cells in low-quality microscopic images with small training sets, achieving promising results for automatic classification.
We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstained images.