Platelet count is a blood test which plays a very important role to evaluate the health as well as to diagnose and follow the process of treatment for a wide range of diseases including leukemia, anemia etc. Typically in primary health care centre's, platelet counting is performed manually which gives inaccurate and unreliable results depending upon the skill of the technician. Another method used for counting platelet is Advia hematology analyzer which is a very expensive machine, not affordable by many rural/remote areas. The intent of our research is to address these challenges. This paper introduces a cost effective automatic platelet counter using a microscope. It attached with an automated camera which is connected to a computer and shoot blood sample images. We considered a concept for counting of platelets in microscopic blood cell images using Circular Hough Transform which uses features such as size and shape of platelets for counting mechanism. Normal blood cells images are used for the results and the examined data produced the accuracy rate of 96% on comparing with manual and machine counting.
The CRISP-DM methodology is commonly used in data analytics exercises within an organisation to provide system and structure to data mining processes. However, in providing a rigorous framework, CRISP-DM overlooks two facets of data analytics in organisational contexts; data mining exercises are far more agile and subject to change than presumed in CRISP-DM and central decisions regarding the interpretation of patterns discovered and the direction of analytics exercises are typically not made by individuals but by committees or groups within an organisation. The current study provides a case study of data mining in a hospital setting and suggests how the agile nature of an analytics exercise and the group reasoning inherent in key decisions can be accommodated within a CRISP-DM methodology.