Adaptive Cycle-consistent Adversarial Network for Malaria Blood Cell Image Synthetization

Malaria is a tropical infectious disease that causes massive global deaths. The convolution neural network (CNN) models can theoretically classify the malaria infected blood cells from normal cells, but they are vulnerable to network attacks even with simple uniform noise. A typical drawback of CNN is that the algorithm cannot properly capture the meaningful patterns with clinical significance. We propose a novel adaptive cycle-consistent adversarial network (Ad Cycle GAN) to synthesize malaria significant patterns based on a homogeneous image template with randomness. The Ad Cycle GAN model consists of a pretrained convolutional variational autoencoder (CVAE) and conventional cycle-consistent adversarial network (Cycle GAN). The CVAE model is trained by a large, segmented blood cell dataset with 27,578 images. The model is optimized for 120 epochs. The CVAE is pipelined to a conventional Cycle GAN model with two generator-discriminator combinations. The real malaria positive images are at first sent to the pretrained CVAE to generate template images for the adversarial optimization with the real images. Therefore, the optimization process is to use generator G to convert the CVAE generated images from the synthetic domain (X) to the real malaria positive image domain (Y), then use generator F to convert the real malaria positive images from the real positive image domain (Y) to the CVAE synthetic image domain (X). The total generator loss is composed of adversarial loss, cycle loss, and identity loss, all loss terms are computed by least squared loss. The Ad Cycle GAN architecture is optimized by 150 epochs. When using a pretrained classifier to differentiate the real and synthetic malaria positive image, 99.61% of the real images from the real image set are accurately recognized, compared to 86.6% of the synthetic images are accurately classified. The average score of Frechet Inception Distance (FID) of the generated images by the Ad Cycle GAN is 0.0053 (Std=0.0004). By human eye observation, the Ad Cycle GAN generated images have reasonable fidelity as real blood cells with meaningful pathological patterns that properly mimics real malaria infected blood cells. The proposed Ad Cycle model can generate synthetic malaria infected blood cell images to successfully optimize the deep neural network model for high classification accuracy. We conclude that the new Ad Cycle GAN model can generate high quality malaria infected blood cell images with good diversity.