Artificial Intelligence for Meiosis and Mitosis Analysis

Abstract

In cellular biology, meiosis and mitosis are essential processes that control cell division and replication as well as the transfer of genetic material. A thorough understanding of these intricate processes is essential for many fields, such as cancer research, genetics, and developmental biology. In this study, we suggest building a Mitosis and Meiosis Analysis System (MMAS) that uses artificial intelligence (AI) methods to make automated analysis and meiotic event characterization easier. The MMAS uses machine learning models, deep learning frameworks, and sophisticated image processing algorithms to precisely recognize and categorize various meiotic and mitotic stages from microscopy images. The MMAS seeks to increase the accuracy and efficiency of cellular biology research while streamlining the analysis process and minimizing manual labor by utilizing artificial intelligence. Furthermore, by providing insightful information about the dynamic character of mitotic and meiotic events, the MMAS helps scientists understand the underlying mechanisms and their implications for a range of physiological and pathological conditions. We hope to improve our knowledge of meiosis and mitosis and hasten research findings in cellular biology by putting the MMAS into practice.