Automatic Healthy Sperm Head Detection using Deep Learning

Abstract

Infertility is one of the diseases in which researchers are interested. Infertility disease is a global health concern, and andrologists are constantly looking for more advanced solutions for this disease. The intracytoplasmic sperm injection (ICSI) process is considered as one of the most common procedures for achieving fertilization. Sperm selection is performed using visual assessment which is dependent upon the skills of the laboratory technicians and as such prone to human errors. Therefore, an automatic detection system is needed for quick and more accurate results. This study utilizes a deep learning technique for the classification of heads of human sperms which indicate the healthy human sperms. The Convolutional Neural Network (CNN) model of visual Geometry Group of 16 layers (VGG16) was used for classification, and it is one of the best architectures used for image classification. The dataset consists of 1200 images of human sperm heads divided into healthy and unhealthy. Here, the VGG16 model is fine-tuned and achieved an accuracy of 97.92% and a sensitivity of 98.82%. Moreover, it achieved an F1 score of 98.53%. The model is an effective and real-time system for detecting healthy sperms that can be injected into eggs for achieving successful fertilization. This model quickly recognizes healthy sperms and makes the sperm selection process more accurate and easier for the andrologists.