Modified Multi-Category Digital Learning Networks for Red Blood Cell Inspection


This paper reports research conducted into classification of red blood cells using multi-category digital learning networks. It is an effective solution for providing healthcare with reduced cost, especially for the rural and far away patients. Digital learning network offer an alternative approach to neural network design. It often referred as( RAM-Based Architectures) , or ( Weightless Neural Networks), since their neurons can be implemented by RAM node that usually input and output binary values with no weight between nodes. The system presented in this paper fulfills the requirements of simplicity and efficiency making it attractive to practical use in present day for industrial and medical environments. Many parameters have been investigated in detail which affects the recognition rate. These parameters are presented to allow the system to be optimized, giving an increase in the performance of the system. Modification method of Feedback Digital Learning Network, which is an improving process of Digital Learning Network, has been implemented. The obtained results showed that high performance can be achieved (96.6% as correct, 2.2% as reject, and 1.1% as error), providing evidence of the validity of the proposed technique.