SynthVision -- Harnessing Minimal Input for Maximal Output in Computer
  Vision Models using Synthetic Image data
Yudara Kularathne Prathapa Janitha Sithira Ambepitiya Thanveer Ahamed Dinuka Wijesundara Prarththanan Sothyrajah
Rapid development of disease detection computer vision models is vital in
response to urgent medical crises like epidemics or events of bioterrorism.
However, traditional data gathering methods are too slow for these scenarios
necessitating innovative approaches to generate reliable models quickly from
minimal data. We demonstrate our new approach by building a comprehensive
computer vision model for detecting Human Papilloma Virus Genital warts using
only synthetic data. In our study, we employed a two phase experimental design
using diffusion models. In the first phase diffusion models were utilized to
generate a large number of diverse synthetic images from 10 HPV guide images
explicitly focusing on accurately depicting genital warts. The second phase
involved the training and testing vision model using this synthetic dataset.
This method aimed to assess the effectiveness of diffusion models in rapidly
generating high quality training data and the subsequent impact on the vision
model performance in medical image recognition. The study findings revealed
significant insights into the performance of the vision model trained on
synthetic images generated through diffusion models. The vision model showed
exceptional performance in accurately identifying cases of genital warts. It
achieved an accuracy rate of 96% underscoring its effectiveness in medical
image classification. For HPV cases the model demonstrated a high precision of
99% and a recall of 94%. In normal cases the precision was 95% with an
impressive recall of 99%. These metrics indicate the model capability to
correctly identify true positive cases and minimize false positives. The model
achieved an F1 Score of 96% for HPV cases and 97% for normal cases. The high F1
Score across both categories highlights the balanced nature of the model
precision and recall ensuring reliability and robustness in its predictions.