Counting of platelets in blood cells assumes to be a significant role in the health sector. In any case, the procedure of the manual tallying of platelets in blood cells is incredibly tedious, which prompts erroneous outcomes. So as to overcome these difficulties, this research presents a fully automated software solution, enriched with image processing and machine learning techniques to distinguish and to count the number of RBC, WBC and Platelets cells in the sample blood images and to classify the different types of viruses present in it. Several problems and missing features in existing white blood cell classifiers were addressed by implementing an effusively automated method using a multiclass classifier.