Blood Cell Analysis Based on Image Processing and Machine Learning Techniques

In order to improve image processing, segmentation, and classification techniques, this study proposes a comprehensive method for analyzing blood cells. By implementing this method, experts in cytology would have the ability to assess blood cells more efficiently and without bias, resulting in a more accurate interpretation of their structural characteristics. As new technologies, like deep learning paradigms, are gaining popularity, it is crucial for both researchers and developers to have access to a comprehensive database of cell images sorted by hospitals and laboratories to aid in their advancements. Identifying blood cancer cells and different lymphocytes solely based on physical characteristics using current technologies is not a viable option. Effective screening protocols should incorporate a comprehensive recognition approach and validate results through laboratory tests. Additionally, the implementation process should prioritize evaluating patient perspectives when considering different classification methods.

Keywords:

image processing , machine learning , classification , segmentation , deep learning