Efficient Compression of Red Blood Cell Image Dataset Using Joint Deep Learning-Based Pattern Classification and Data Compression

Millions of people across the globe are affected by the life-threatening disease of Malaria. To achieve the remote screening and diagnosis of the disease, the rapid transmission of large-size microscopic images is necessary, thereby demanding efficient data compression techniques. In this paper, we argued that well-classified images might lead to higher overall compression of the images in the datasets. To this end, we investigated the novel approach of joint pattern classification and compression of microscopic red blood cell images. Specifically, we used deep learning models, including a vision transformer and convolutional autoencoders, to classify red blood cell images into normal and Malaria-infected patterns, prior to applying compression on the images classified into different patterns separately. We evaluated the impacts of varying classification accuracy on overall image compression efficiency. The results highlight the importance of the accurate classification of images in improving overall compression performance. We demonstrated that the proposed deep learning-based joint classification/compression method offered superior performance compared with traditional lossy compression approaches such as JPEG and JPEG 2000. Our study provides useful insights into how deep learning-based pattern classification could benefit data compression, which would be advantageous in telemedicine, where large-image-size reduction and high decoded image quality are desired.