Classification of Blood Cells from Microscopic Images Using Shallow CNN

Key Points

• The study aims to classify blood cells using shallow convolutional neural networks from microscopic images.
• Utilized shallow CNN for image analysis of blood cells.
• Collected and annotated microscopic images of various blood cells.
• Applied deep learning techniques for classification accuracy assessment.
• Achieved a classification accuracy of over 90%, demonstrating effectiveness of shallow CNN.
• Showed improved identification of different blood cell types compared to traditional methods.