Infertility is a crucial reproductive problem experienced by both men and women. Infertility is the inability to get pregnant within one year of sexual intercourse. This study focuses on infertility in men. Many causes that can cause infertility in men including sperm quality. Currently, identification of human sperm is still done manually by observing the sperm with the help of humans through a microscope, so it requires time and high costs. Therefore, high technology is needed to determine sperm quality in the form of deep learning technology based on video. Deep learning algorithms support this research in identifying human sperm cells. So deep learning can help detect sperm video automatically in the process of evaluating sperm cells to determine infertility. We use deep learning technology to identify sperm using the You Only Look Once version 4 (YOLOv4) algorithm. Purpose of this study was to analyze the level of accuracy of the YOLOv4 algorithm. The dataset used is sourced from a VISEM dataset of 85 videos. The results obtained are 90.31% AP (Average Precision) for sperm objects and 68.19% AP (Average Precision) for non-sperm objects, then for the results of the training obtained by the model 79.58% mAP (Mean Average Precision). Our research show result about identification of human sperm using YOLOv4. The results obtained by the YOLOv4 model can identify sperm and non-sperm objects. The output on the YOLOv4 model is able to identify objects in the test data in the form of video and image.
Skin diseases are a group of diseases affecting people of all ages, commonly caused by fungi, bacteria, parasites, viruses, and infections. The disease's main symptoms are usually itching all over the skin. Many patients are often underestimated and embarrassed to consult directly with doctors, which in the end, ignores the symptoms of skin diseases. Since they usually have imprecision symptoms, examining skin diseases is complex and challenging. Recently, many efforts have been made to utilize artificial intelligence approaches for diagnosing various diseases based on the patient's condition. This paper aims to develop a novel fuzzy-based medical expert system based on unprecise existing symptoms. The system uses the specialist Doctor's knowledge (dermatologist) to diagnose and provide the patient's severity level for the disease. We have done numerical experiments using 100 (one hundred) test problems to evaluate the performance of the developed system by comparing the result with the recommendations of doctors (dermatologists). It shows that this system succeeds in all tests with an accuracy value of 95.6%. Thus, this system is very beneficial to support doctors in the assessment of skin diseases.
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