ชุดกล้องอัจฉริยะ Microsis DCN ระบบการนับและแยกเซลล์เม็ดเลือดแดง เม็ดเลือดขาวและเกร็ดเลือด

Summary

This presentation introduces an innovative blood cell classification system developed by Chulalongkorn University researchers. The system uses machine learning and a specialized microscope attachment to automatically count and classify red blood cells, white blood cells, and platelets. It offers a faster, more accurate alternative to manual counting, with applications in areas lacking automated systems. The project has earned recognition, including awards at a national research expo, and invites collaboration in digital image processing to enhance healthcare solutions. This innovation promises significant benefits in medical diagnostics and disease analysis.

Takeaways

• 😀 The research project focuses on developing a smart camera system for automatic blood cell analysis.
• 😀 The system aims to improve the detection and counting of red blood cells, white blood cells, and platelets.
• 😀 The project was funded by the Ratchada Phisek Fund for Innovations in 2019.
• 😀 Traditional manual methods of blood cell counting can be slow and prone to error, especially in remote areas.
• 😀 The system uses machine learning to train the camera to recognize specific features of different blood cells.
• 😀 The system can analyze and count cells within two minutes, much faster than the manual process, which can take several minutes.
• 😀 The system is designed to be user-friendly and can be easily installed on a standard microscope.
• 😀 The innovation aims to reduce human error and fatigue in experts who manually count blood cells.
• 😀 The research has been applied to detect diseases such as anemia, leukemia, and malaria through blood sample analysis.
• 😀 The project received national recognition, winning a gold medal at the 2020 National Research Expo.
• 😀 The researchers encourage students and professionals interested in digital image processing and computer vision to engage with the technology for public benefit.

Q & A

What is the main focus of the research project presented in the transcript?

The main focus of the research project is the development of a smart camera system that uses machine learning to automatically count and classify blood cells, including red blood cells, white blood cells, and platelets.

How does the smart camera system work in classifying blood cells?

The system uses machine learning to automatically recognize and classify blood cells by analyzing images captured through a microscope. It can distinguish between red blood cells, white blood cells, and platelets, providing accurate counts for each cell type.

Why is this system particularly important for remote areas?

In remote areas, medical professionals may not have access to automated systems, and manual cell counting can be time-consuming and prone to human error. The smart camera system enables faster, more accurate cell classification and is designed to be easy to use, even in areas with limited resources.

What is the key innovation in the research project’s smart camera system?

The key innovation is the *Micro Seed DCN* system, which attaches to the eyepiece of a microscope. This system enhances the microscope's capabilities, enabling automatic blood cell counting and classification, significantly improving the efficiency of medical diagnostics.

How does the *Micro Seed DCN* system improve upon traditional microscopes?

The *Micro Seed DCN* system transforms a traditional microscope by allowing it to automatically count and classify blood cells through machine learning, reducing the need for manual counting and improving accuracy and efficiency in medical diagnostics.

What diseases can the system help detect and classify through blood analysis?

The system can help detect and classify blood cells related to diseases such as anemia, leukemia, malaria, and bone marrow cancers by analyzing blood samples for abnormal cell patterns.

How does the system handle the process of blood sample analysis?

The system captures blood sample images through a microscope, which are then analyzed using machine learning models trained on labeled data. The models classify the blood cells into categories like red blood cells, white blood cells, and platelets, providing results in under two minutes.

What role does machine learning play in this research project?

Machine learning plays a critical role by training the system to recognize different types of blood cells based on image data. It helps the system improve its accuracy in classifying cells and speeds up the diagnostic process, reducing the need for manual intervention.

What were the results of the project in terms of recognition and achievement?

The project received significant recognition, including winning a gold medal and being awarded first place at the National Research Expo in 2020, for its innovation in healthcare technology and its practical application in blood cell classification.

Who were the key contributors to the research project?

The project was a collaborative effort involving Dr. Surii Phumrin from Chulalongkorn University, students from the Department of Electrical Engineering, and medical professionals specializing in hematology. The research team also worked with experts in blood cell analysis and machine learning.