HivNet: Studying in Depth the Morphology of HIV-1 Virion Using Deep Learning

Parth Pandey; Himanshu Pandey; Khushi Srivastava

doi:10.18502/fbt.v10i4.13730

Parth Pandey Department of Computer Science and Engineering, Faculty of Engineering and Technology, University of Lucknow, Lucknow, India
Himanshu Pandey Department of Computer Science and Engineering, Faculty of Engineering and Technology, University of Lucknow, Lucknow, India
Khushi Srivastava Department of Computer Science and Engineering, Faculty of Engineering and Technology, University of Lucknow, Lucknow, India

DOI: https://doi.org/10.18502/fbt.v10i4.13730

Keywords: HIV-1 Virology; Artificial Intelligence; Deep Learning; Computer Vision; Electron Microscopy; Convolutional Neural Networks.

Abstract

Purpose: Human Immunodeficiency Virus (HIV) continues to be a disease that kills thousands of individuals each year. The HIV infection is incurable. However, HIV infection has turned into a treatable chronic health condition because of improved access to efficient HIV prevention, diagnosis, treatment, and care. Transmission Electron Microscopy’s (TEM) ability to directly visualize virus particles and distinguish ultrastructure morphology at the nanometer scale, makes it useful in HIV-1 research where it is used for assessing the actions of inhibitors that obstruct the maturation and morphogenesis phases of the virus lifecycle. Hence with its use, the disease's serious stage can be avoided by receiving an early diagnosis.

Materials and Methods: Through the dedicated use of computer vision frameworks and machine learning techniques, we have developed an optimized low-computational-cost 8-layer Convolutional Neural Network (CNN) backbone capable of classifying HIV-1 virions at various stages of maturity and morphogenesis. The dataset including TEM images of HIV-1 viral life cycle phases is analysed and augmented through various techniques to make the framework robust in real-time. The CNN layers then extract pertinent disease traits from TEM images and utilise them to provide diagnostic predictions.

Results: It was discovered that the framework performed with an accuracy of 99.76% on the training set, 85.83% on the validation set, and 91.33% on the test set, after being trained on a wide range of micrographs which comprised of different experimental samples and magnifications.

Conclusion: The suggested network's performance was compared to that of other state-of-the-art networks, and it was discovered that the proposed model was undisputed for classifying TEM images of unseen HIV-1 virion and required less time to train and tweak its weights. The framework can operate more effectively than machine learning algorithms that consume a lot of resources and can be deployed with limited computation and memory resource requirements.