Some blood cell concerns need to be addressed in order to solve them. One of the issues is the matter of the blood corpuscle itself. Capell [6] say that their method fails to classify some of the blood cells in the process of classification. Thanks to environmental strain, a number of cells are also deformed into an arbitrary shape [3].
The Notes on their algorithms that do not distinguish cells that overlap. Overlapping cells may also be joined by disease-induced cells [23].
He argued that identification between two neighboring cells in their development line is the most difficult problem since the cells are very close and thus the boundary point between two neighbors is not well defined. But overlapping problems are also solved using the watershed approaches used in their studies [6]. Another challenge is information collection. Blood sample images should be adequate to ensure that generalization properties are always expressed and that unseen data can be identified correctly [6]. Lacking samples means that the info [5] can only be represented by a few key components [24]. Believes that separate data sets are the perfect route should be used for each point. However, cross validation or bootstrap sampling can also be used as it is difficult to collect a sizeable amount of samples. It is hoped that the approach of RL in the classification phase would reduce the issue of inadequate data. Once we are to build the method, all the problems posed by scientists have to be taken into account. By applying effective strategies, we should always strive to resolve them.
5 Conclusion
This study includes using microscopic blood sample images to classify the forms of leukaemia. By using features in microscopic images, the device will be constructed by analysing changes in texture, geometry, colours and statistical analysis as a classifier input. The system should be effective, accurate, less time interval, smaller error, and high precision, cheaper cost and robust for individual varieties, sample collection protocols, time and so on. Knowledge derived from microscopic blood sample images will support individuals by rapidly predicting, resolving and treating blood disorders for a particular patient.
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