Genetic Neural Architecture Search for automatic assessment of human sperm images

Male infertility is a disease which affects approximately 7% of men. Sperm morphology analysis (SMA) one the main diagnosis methods for this problem. Manual SMA an inexact, subjective, non-reproducible, and hard to teach process. As result, in paper, we introduce novel automatic based on neural architecture search algorithm termed Genetic Neural Architecture Search (GeNAS). For purpose, used collection images called MHSMA dataset contains 1,540 sperm have been collected from 235 patients with problems. GeNAS genetic that acts as meta-controller explores constrained space plain convolutional network architectures. Every individual trained predict morphological deformities different segments human (head, vacuole, acrosome), its fitness calculated by proposed method named GeNAS-WF especially designed noisy, low resolution, imbalanced datasets. Also, hashing save each fitness, so could reuse them during evaluation speed up algorithm. Besides, terms running time computation power, our far more efficient than most other existing algorithms. Additionally, evaluated balanced datasets, whereas built specifically quality, datasets are common field medical imaging. In experiments, best found has reached accuracy 91.66%, 77.33%, 77.66% head, acrosome abnormality detection, respectively. comparison algorithms dataset, achieved state-of-the-art results.