A fully automated hybrid human sperm detection and classification system based on mobile-net and the performance comparison with conventional methods.

Medical & Biological Engineering & Computing
Hamza O IlhanNizamettin Aydin

Abstract

Sperm morphology, as an indicator of fertility, is a critical tool in semen analysis. In this study, a smartphone-based hybrid system that fully automates the sperm morphological analysis is introduced with the aim of eliminating unwanted human factors. Proposed hybrid system consists of two progressive steps: automatic segmentation of possible sperm shapes and classification of normal/ab-normal sperms. In the segmentation step, clustering techniques with/without group sparsity approach were tested to extract region of interests from the images. Subsequently, a novel publicly available morphological sperm image data set, whose labels were identified by experts as non-sperm, normal and abnormal sperm, was created as the ground truths of classification step. In the classification step, conventional and ensemble machine learning methods were applied to domain-specific features that were extracted by using wavelet transform and descriptors. Additionally, as an alternative to conventional features, three deep neural network architectures, which can extract high-level features from raw images after using statistical learning, were employed to increase the proposed method's performance. The results show that, for the conventional feat...Continue Reading

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Citations

Dec 31, 2020·Sensors·Viktorija ValiuškaitėTomas Krilavičius

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Methods Mentioned

BETA
feature extraction

Software Mentioned

HuSHeM
MobileNet
VGG19
Morpho
Python Keras
mathbb
Keras
CASA
MOGS
Tensorflow

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