یک راهکار نیمه‌نظارتی جدید برای انتخاب اسپرم مناسب در درمان ناباروری

محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند

آصفه توکلی پله شاهی ¹ , سیدابوالقاسم میرروشندل ² , فاطمه قاسمیان ³

1 - دانشجوی کارشناسی ارشد / دانشگاه گیلان
2 - عضو هیات علمی / گروه مهندسی کامپیوتر، دانشکده فنی، دانشگاه گیلان
3 - عضور هیات علمی / گروه زیست شناسی، دانشکده علوم، دانشگاه گیلان

تاریخ دریافت : 1400/12/19 تاریخ پذیرش : 1401/07/20 تاریخ انتشار : 1400/01/01

کلید واژه: یادگیری عمیق نیمه نظارتی, ناباروری, آنالیز خودکار تصاویر, مورفولوژی اسپرم انسان,

چکیده مقاله :

: امروزه، رشد چشمگیر ناباروری در جوامع مختلف و نیاز به بررسی هر یک از عوامل ناشی از ناتوانی مردان و زنان در ایجاد و تشدید آن بر کسی پوشیده نیست. در این میان، آمارهای سازمان بهداشت جهانی از رشد سریع عوامل ناتوانی مردانه در باروری تا حدود 50 درصد حکایت دارد؛ که نشان‌دهنده اهمیت بالای تجزیه و تحلیل مورفولوژیکی اسپرم، به عنوان یکی از مهم‌ترین و اساسی‌ترین گام‌ها در تجزیه و تحلیل مایع منی، به منظور اجرای لقاح مصنوعی است. در این مقاله، تلاش شده‌است تا به کمک یکی از روش‌های یادگیری نیمه‌نظارتی، موسوم به شبکه نردبانی، به استخراج ویژگی‌های بخش‌های مختلف اسپرم (مانند سر، واکوئل و آکروزوم) پرداخته و در ادامه با طبقه‌بندی آن‌ها در دو گروه اسپرم‌های طبیعی و غیرطبیعی، به انتخاب اسپرم مناسب، به منظور شرکت در فرآیند لقاح مصنوعی موفق شویم. پژوهش حاضر با اعمال تغییرات و بهبود عوامل مختلف به‌ویژه نویز ورودی، نتایج مناسبی را در آنالیز تصاویر با وضوح پایین و بدون رنگ‌آمیزی کسب کرده است. بررسی‌ مدل پیشنهادی برای هر سه بخش اسپرم (سر، واکوئل و آکروزوم) موفق شد با وجود تصاویری با کیفیت پایین، نتایج چشم‌گیر بیش از 70% را برای سر و آکروزوم و بیش از 80% را برای واکوئل به‌دست آورد.

چکیده انگلیسی:

Introduction:Nowadays infertility is recognized as one of the most common clinical problems around the globe, and one of the most worrying social issues in different cultures and societies. In the meantime, efforts have always been made to prevent the progression of infertility caused by this factor by carefully examining the most effective male factor - as one of the potential parties in infertility problems - that is, analyzing the quantity and quality of sperm. On one hand, traditional methods have lots of problems such as inadequate accuracy, clinicians' disagreements, and prolonged treatment. On the other hand, the successes of machine learning in many areas prompted researchers to move toward automating sperm morphology analysis by means of machine learning.Methods:The ladder network as a semi-supervised learning algorithm, by using a small number of labeled samples and a larger part of unlabeled data, shows suitability and compliance with the real-world requirements in this field of study. In this regard, in order to implement ladder networks, the structure of stack noise removal auto-encoders with the architecture of two parallel encoders has been used to represent the samples and a decoder to reconstruct the samples. The present study by applying changes and improving various factors, especially input noise, has obtained good results in the analysis of low-resolution images without coloring.Results:The proposed model succeeded by extracting positive and fruitful features from the images of the head, acrosome, and vacuole of human sperm, showing an acceptable accuracy for classifying them into two natural and abnormal classes, and finally selecting the appropriate sperm to participate in the artificial insemination process. The study of the proposed model for all three sperm sections (head, vacuole, and acrosome) succeeded, despite low-quality images, achieving impressive results of more than 70% for the head and acrosome and more than 80% for the vacuole.Conclusion: In the future, we intend to improve the proposed model by finding ways to increase the accuracy and reduce the error of the test results and show that the change in the type of noise or how it is applied to the network will have a significant impact on the network performance.

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