A vision transformer based approach for analysis of plasmodium vivax life cycle for malaria prediction using thin blood smear microscopic images. (September 2022) - Sengar, Neha; Burget, Radim; Dutta, Malay Kishore
Highlights: Deep learning based efficient classification of plasmodium vivax life cycle to diagnose malaria disease using thin blood smear microscopic images. Automatic, real-time and robust to real possible inputs and accuracy achieved up to 90.03% on unseen images. Approach targets complete lifecycle of plasmodium vivax malaria species. Analysis of different state-of-the-art deep learning models and vision transformer on microscopic dataset. Abstract: Background and objectives: Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Changes in blood cells are caused by malaria disease, and early diagnosis can prevent the disease from entering its severe stage. Methods: In this paper, an automated non-invasive and efficient deep learning-based framework is developed for multi-class plasmodium vivax life cycle classification and malaria diagnosis. A multi-class microscopic blood cell of different plasmodium vivax life cycle stage dataset is analysed, and a diagnostic framework is designed. Several stages of the disease are examined and augmented through various techniques to make the framework robust in real-time. Generative adversarial network is specially designed to generate extended training samples of various life cycle stages to increase robustness of the resulting model. A special transformer-based neural network vision transformer is designed to improve generalisation capabilities. Microscopic images are classified into multi classes of plasmodium vivax life cycle stage, where the keystone transformer layers extract relevant disease features from microscopic colour images, and the extracted relevant features are used to make predictive diagnostic decisions. Results: The capabilities of the vision transformer are computed and analysed by statistical parameters, and the performance of the vision transformer model is compared with baseline architectures, where it was evident that the performance of the vision transformer was significantly better, reaching 90.03% accuracy. Conclusions: A comprehensive comparison of the proposed framework to the state-of-the-art methods proves its efficiency in the classification of plasmodium vivax life cycle for malaria disease identification through thin blood smear microscopic images.