Genetic Neural Architecture Search for automatic assessment of human sperm images

Male infertility is a disease that affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem. However, manual SMA is an inexact, subjective, non-reproducible, and hard to teach process. Therefore, in this paper, we introduce a novel automa...

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Bibliographic Details
Published in:Expert systems with applications Vol. 188; p. 115937
Main Authors: Miahi, Erfan, Mirroshandel, Seyed Abolghasem, Nasr, Alexis
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.02.2022
Elsevier BV
Elsevier
Subjects:
Artificial Intelligence
Artificial neural networks
Bioinformatics
Computer architecture
Computer Science
Datasets
Deep learning
Evaluation
Fitness
Genetic algorithm
Genetic algorithms
Human sperm morphometry
Infertility
Machine learning
Medical imaging
Morphology
Neural architecture search
Neural networks
Search algorithms
Search methods
Sperm
Deep learning
Neural architecture search
Human sperm morphometry
Genetic algorithm
Infertility
Human Sperm Morphometry
Neural Architecture Search
Deep Learning
Genetic Algorithm
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:Male infertility is a disease that affects approximately 7% of men. Sperm morphology analysis (SMA) is one of the main diagnosis methods for this problem. However, manual SMA is an inexact, subjective, non-reproducible, and hard to teach process. Therefore, in this paper, we introduce a novel automatic SMA technique that is based on the neural architecture search algorithm, named Genetic Neural Architecture Search (GeNAS). For this purpose, we used a collection of images termed MHSMA dataset, which contains 1540 sperm images that have been collected from 235 patients with infertility problems. In detail, GeNAS consists of a special genetic algorithm that acts as a meta-controller which explores the constrained search space of plain convolutional neural network architectures. Every individual of this genetic algorithm is a convolutional neural network trained to predict morphological deformities in different segments of human sperm (head, vacuole, and acrosome). The fitness of each individual is calculated by a novel proposed method, named GeNAS Weighting Factor (GeNAS-WF). This technique is specially designed to evaluate the fitness of neural networks which, during their learning process, validation accuracy highly fluctuates. To speed up the algorithm, a hashing method is practiced to save each trained neural architecture fitness, so we could reuse them during fitness evaluation. In terms of running time and computational power, our proposed architecture search method is far more efficient than most of the other existing neural architecture search algorithms. Moreover, whereas most of the existing neural architecture search algorithms are designed to work well with well-prepared benchmark datasets, the overall paradigm of GeNAS is specially designed to address the challenges of real-world datasets, particularly shortage of data and class imbalance. In our experiments, the best neural architecture found by GeNAS has reached an accuracy of 91.66%, 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. •A novel neural architecture search based on Genetic Algorithm.•A novel neural architecture encoding for exploration of search space.•A specific Crossover and Mutation operations for neural architecture search.•Work on imbalanced datasets, low resolution images, and non-stained sperms.•State-of-the-art accuracy & Real-time sperm morphological analysis.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115937