Abstract
Principal component analysis (PCA) is based feature reduction that reduces the correlation of features. In this research, a novel approach is proposed by applying the PCA technique on various morphologies of red blood cells (RBCs). According to hematologists, this method successfully classified 40 different types of abnormal RBCs. The classification of RBCs into various distinct subtypes using three machine learning algorithms is important in clinical and laboratory tests for detecting blood diseases. The most common abnormal RBCs are considered as anemic. The RBC features are sufficient to identify the type of anemia and the disease that caused it. Therefore, we found that several features extracted from RBCs in the blood smear images are not significant for classification when observed independently but are significant when combined with other features. The number of feature vectors is reduced from 271 to 8 as time resuming in training and accuracy percentage increased to 98%.
Highlights
Analysing medical images and processing them has a great significance as it helps in identifying as well as treating various blood diseases and in performing clinical studies
The variation of the morphology of red blood cells (RBCs) is an indication of the different types of blood diseases [2]
Many variations found in the morphology of the RBCs images make it really difficult for them to be detected by machines because of their similarity in shape, size and colour [3]
Summary
Analysing medical images and processing them has a great significance as it helps in identifying as well as treating various blood diseases and in performing clinical studies. These imaging techniques help the doctors and the biologists to reach a diagnosis [1]. The number of features is kept at a minimal when a larger decimation factor is applied [4]. This means that as the quality of the analysis is reduced, low classification accuracy is achieved. The qualitative measure of the performance of a supervised classification algorithm is its accuracy with as low training samples as possible [6]
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