Scholarlink

<p>Mitosis, the process of cell division in somatic cells, occurs in distinct stages: interphase, prophase, metaphase, anaphase, and telophase.&#xa0;Understanding these phases is fundamental to studying the growth and development of&#xa0;living organisms like humans, animals, and plants. Modern imaging methods have produced large volumes of microscopic data, opening the door to in-depth cellular study. Traditionally, mitotic phase identification was made by humans using manual methods that were not only time-consuming and labor-intensive but were also highly susceptible to human mistakes. These challenges highlight the growing need for automated solutions. This study examines the limitations of conventional techniques and investigates how deep learning (DL) and machine learning (ML) have become effective methods for mitotic phase detection. ML algorithms analyze patterns in microscopic images, and DL, particularly utilizing convolutional neural networks (CNNs), can automatically extract features and recognize complex structures with high precision. This study demonstrates how ML and DL techniques overcome the limitations of manual techniques by offering scalable, precise, and reliable solutions. The study also contrasts ML with DL approaches, showing how deep learning significantly improves the accuracy of recognition of mitotic phases by leveraging large datasets and hierarchical feature learning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Machine learning and deep learning techniques for identification of phases of mitosis in plant cells: challenges, innovations, and prospective pathways

  • 期刊论文

  • 发表日期：

    2025年7月26日

查看全文
  • Kritika Pandey,
  • Sanskruti Patel,
  • Gayatri Dave

摘要

Mitosis, the process of cell division in somatic cells, occurs in distinct stages: interphase, prophase, metaphase, anaphase, and telophase. Understanding these phases is fundamental to studying the growth and development of living organisms like humans, animals, and plants. Modern imaging methods have produced large volumes of microscopic data, opening the door to in-depth cellular study. Traditionally, mitotic phase identification was made by humans using manual methods that were not only time-consuming and labor-intensive but were also highly susceptible to human mistakes. These challenges highlight the growing need for automated solutions. This study examines the limitations of conventional techniques and investigates how deep learning (DL) and machine learning (ML) have become effective methods for mitotic phase detection. ML algorithms analyze patterns in microscopic images, and DL, particularly utilizing convolutional neural networks (CNNs), can automatically extract features and recognize complex structures with high precision. This study demonstrates how ML and DL techniques overcome the limitations of manual techniques by offering scalable, precise, and reliable solutions. The study also contrasts ML with DL approaches, showing how deep learning significantly improves the accuracy of recognition of mitotic phases by leveraging large datasets and hierarchical feature learning.