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• W3033235005 endingPage "103845" @default.
• W3033235005 startingPage "103845" @default.
• W3033235005 abstract "Sperm Morphology is the key step in the assessment of sperm quality. Due to the effect of misleading human factors in manual assessments, computer-based techniques should be employed in the analysis. In this study, a computation framework including multi-stage cascade connected preprocessing techniques, region based descriptor features, and non-linear kernel SVM based learning is proposed for the classification of any stained sperm images for the assessment of the morphology. The proposed framework was evaluated on two sperm morphology datasets: the Human Sperm Head Morphology dataset (HuSHeM) and Sperm Morphology Image Data Set (SMIDS). The results indicate that cascading the preprocessing techniques used in the proposed framework, such as wavelet based local adaptive de-noising, modified overlapping group shrinkage, image gradient, and automatic directional masking, increased the classification accuracy by 10% and 5% for the HuSHeM and SMIDS, respectively. The proposed framework results in better overall accuracy than most state-of-the-art methods, while having significant advantages, such as eliminating the exhaustive manual orientation and cropping operations of the competitors with reasonable rates of consumption of time and source." @default.
• W3033235005 created "2020-06-12" @default.
• W3033235005 creator A5022727512 @default.
• W3033235005 creator A5061586060 @default.
• W3033235005 creator A5081837964 @default.
• W3033235005 date "2020-07-01" @default.
• W3033235005 modified "2023-10-13" @default.
• W3033235005 title "Automated sperm morphology analysis approach using a directional masking technique" @default.
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• W3033235005 doi "https://doi.org/10.1016/j.compbiomed.2020.103845" @default.
• W3033235005 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32658734" @default.
• W3033235005 hasPublicationYear "2020" @default.
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