Deep-learning based model for sperm morphology assessment using the SMD/MSS dataset

Manual sperm morphology assessment is recognized as a challenging parameter to standardize due to its subjective nature, often reliant on the operator's expertise. Our study aims to address this issue by developing a predictive model for sperm morphological evaluation utilizing artificial neural networks trained on the SMD/MSS(Sperm Morphology Dataset/Medical School of Sfax) dataset enhanced through data augmentation techniques.

A total of 1000 images of individual spermatozoa were acquired using the MMC CASA system. Expert classification, based on the modified David classification for sperm morphology, was conducted by three experts. Data augmentation techniques were employed to augment the database. Subsequently, an algorithm utilizing a Convolutional Neural Network (CNN) was created, trained, and tested for spermatozoa classification.

SMD/MSS dataset, initially comprised 1000 images and extended to 6035 images after the application of data augmentation techniques. The deep learning model produced satisfactory results, with an accuracy ranging from 55% to 92%.

Our deep learning approach for sperm morphology classification enables the automation, standardization, and acceleration of semen analysis. It underscores the significance of artificial intelligence in medical applications, with a particular focus on its impact in the field of reproductive biology.