A review of different deep learning techniques for sperm fertility prediction

Sperm morphology analysis (SMA) is a significant factor in diagnosing male infertility. Therefore, healthy sperm detection is of great significance in this process. However, the traditional manual microscopic sperm detection methods have the disadvantages of a long detection cycle, low detection accuracy in large orders, and very complex fertility prediction. Therefore, it is meaningful to apply computer image analysis technology to the field of fertility prediction. Computer image analysis can give high precision and high efficiency in detecting sperm cells. In this article, first, we analyze the existing sperm detection techniques in chronological order, from traditional image processing and machine learning to deep learning methods in segmentation and classification. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. Finally, the future development direction and challenges of sperm cell detection are discussed. We have summarized 44 related technical papers from 2012 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of fertility prediction and provide a reference for researchers in other fields

A Flexible Approach to High Level Simulation of Complex System-on-Chip

Abstract – This paper presents a method to create a model for simulating the top-level system design of System-on-Chip using a sequential language. The model constitutes a cycle accurate simulation of the system under test at the required level of abstraction. The need for such a configurable platform arises during the initial system level design phase. The approach described in the paper has been successfully applied in the system level verification of a commercial media processor where the primary application is in VoIP media gateways. The implementation is primarily focused on monitoring and verification of inter-processor communication (IPC) among processing engines in the media processor. I

Sperm Abnormality Detection Using Sequential Deep Neural Network

Sperm morphological analysis (SMA) is an essential step in diagnosing male infertility. Using images of human sperm cells, this research proposes a unique sequential deep-learning method to detect abnormalities in semen samples. The proposed technique identifies and examines several components of human sperm. In order to conduct this study, we used the online Modified Human Sperm Morphology Analysis (MHSMA) dataset containing 1540 sperm images collected from 235 infertile individuals. For research purposes, this dataset is freely available online. To identify morphological abnormalities in different parts of human sperm, such as the head, vacuole, and acrosome, we proposed sequential deep neural network (SDNN) architecture. This technique is also particularly effective with low-resolution, unstained images. Sequential deep neural networks (SDNNs) demonstrate high accuracy in diagnosing morphological abnormalities based on the given dataset in our tests on the benchmark. Our proposed algorithm successfully detected abnormalities in the acrosome, head, and vacuole with an accuracy of 89%, 90%, and 92%, respectively. It is noteworthy that our system detects abnormalities of the acrosome and head with greater accuracy than current state-of-the-art approaches on the suggested benchmark. On a low-specification computer/laptop, our algorithm also requires less execution time. Additionally, it can classify photos in real time. Based on the results of our study, an embryologist can quickly decide whether to use the given sperm