Scholarlink

Red blood cells (RBCs) also known as erythrocytes are an essential component of the human body that play several important roles. RBCs transport oxygen \((\textrm{O}_2)\) from the lungs to the body’s tissues, remove carbon dioxide \((\textrm{CO}_2)\) , regulate pH balance, support the immune system, and provide diagnostic information. The abnormal shapes of RBCs (signified as poikilocytosis) are unable to carry \(\textrm{O}_2\) and \(\textrm{CO}_2\) , leading to decreased \(\textrm{O}_2\) delivery to the tissues and an increased workload on the heart and lungs. Anemia, thalassemia, and other blood-related illnesses affect the body for insufficient replenishment (oxygen, protein, nutrients). Hematologists take more time to examine RBC shapes manually using a microscope. In this study ensemble deep learning technique was introduced to examine poikilocytosis abnormality accurately and efficiently. The proposed ensemble model outperforms state-of-the-art approaches with an excellent accuracy and \(F_1\) -score of \(98.77\%\) .

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

Classification of Poikilocytosis Abnormality Using Ensemble Deep Learning Technique

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Prasenjit Dhar,
  • Mohit Kumar Singh,
  • Tanveer Ahmed,
  • Devi K. Suganya

摘要

Red blood cells (RBCs) also known as erythrocytes are an essential component of the human body that play several important roles. RBCs transport oxygen \((\textrm{O}_2)\) from the lungs to the body’s tissues, remove carbon dioxide \((\textrm{CO}_2)\) , regulate pH balance, support the immune system, and provide diagnostic information. The abnormal shapes of RBCs (signified as poikilocytosis) are unable to carry \(\textrm{O}_2\) and \(\textrm{CO}_2\) , leading to decreased \(\textrm{O}_2\) delivery to the tissues and an increased workload on the heart and lungs. Anemia, thalassemia, and other blood-related illnesses affect the body for insufficient replenishment (oxygen, protein, nutrients). Hematologists take more time to examine RBC shapes manually using a microscope. In this study ensemble deep learning technique was introduced to examine poikilocytosis abnormality accurately and efficiently. The proposed ensemble model outperforms state-of-the-art approaches with an excellent accuracy and \(F_1\) -score of \(98.77\%\) .