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) is one of the main diagnosis methods for this problem. Manual SMA is an inexact, subjective, non-reproducible, and hard to teach process. As a result, in this paper, we introduce a novel automatic SMA that is based on a neural architecture search algorithm called Genetic Neural Architecture Search (GeNAS). For this purpose, we used a collection of images called MHSMA dataset which contains 1, 540 sperm images that have been collected from 235 patients with infertility problems. GeNAS is a genetic algorithm that acts as a meta-controller which explores the constrained search space of plain convolutional neural network architectures. Every individual of the genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome), and its fitness is calculated by a novel proposed method called GeNAS-WF that is specially designed for noisy, low resolution, and imbalanced datasets. Also, a hashing method is used to save each trained neural architecture fitness, so we could reuse them during fitness evaluation and speed up the algorithm. Besides, in terms of running time and computation power, our proposed architecture search method is far more efficient than most of the other existing neural architecture search algorithms. Additionally, other proposed methods have been evaluated on balanced datasets, whereas GeNAS is built specifically for noisy, low quality, and imbalanced datasets which are common in the field of medical imaging. In our experiments, the best neural architecture found by GeNAS has reached an accuracy of 92.66%, 77.33%, and 77.66% in the vacuole, head, and acrosome abnormality detection, respectively. In comparison to other proposed algorithms for MHSMA dataset, GeNAS achieved state-of-the-art results. Approximately 15% of couples suffer from infertility, which is in 30 to 40% of the cases due to the male sperm abnormalities [1], [2]. One of the key methods for male infertility diagnosis is sperm morphology analysis (SMA) which consists of classifying sperm head, vacuole, and acrosome as normal or abnormal. In this paper, we propose an automatic SMA system that is based on Convolutional Neural Networks (CNN). CNN is a natural choice for such a task for they have shown very good performances for image classification [3], [4], [5].