SEGREGATION OF MEDICAL WASTES USING FEEDFORWARD NEURAL NETWORKS AND IMAGE PROCESSING FOR A NEW CLASSIFICATION

Ramani  Bai Varadharajan; Kangadharan  Gopinath; Mohd  Razman Bin Salim; Gopinath  Ramadas; Dhanush  Gopinath

Authors

Ramani Bai Varadharajan Department of Civil Engineering, FETBE, UCSI University, Malaysia.
Kangadharan Gopinath Asia Pacific University of Technology & Innovation, Malaysia.
Mohd Razman Bin Salim Department of Civil Engineering, FETBE, UCSI University, Malaysia.
Gopinath Ramadas Babas Products (M) Sdn. Bhd., Selayang, West Malaysia.
Dhanush Gopinath Kensington Academy, Malaysia.

Keywords:

Artificial neural networks, Image processing, Centroidal profile, Shape recognition, Medical waste

Abstract

Medical waste includes all types of waste that are produced by medical institutions, medical research centers, or laboratories. Medical waste has highly toxic substances, pathogenic viruses, and other pathogens, making it a major public health concern. Immune deficiency virus (HIV), hepatitis B virus (HBV), and some other agents related to blood diseases could be transmitted by medical waste. Thus, the study aims to develop a proper medical waste classification and its’ respective colour-coding system. The effectiveness of the artificial neural network (ANN) in the recognition of image patterns of clinical wastes is also studied in this research. The existing classification system does not segregate medical waste according to its characteristics. Moreover, the same product has been divided into different categories. The pharmaceutical and cytotoxic pharmaceutical wastes are not classified as hazardous waste. The chemical, radioactive and recyclable wastes have been omitted from the classification. The current medical waste segregation method did not have separate containers for the subclasses of medical waste. By adopting these classifications and models developed in this study one can save up to 80% of their current expenditure on medical waste disposal. Further, it would reduce; cost, improper disposal, the atmospheric release of carcinogenic agents, and infection from a medical source to the wider public. Hence current developed ANN model would be further used to create an automated waste segregation instrument (named, ‘AUTOM’) which is commercially produced for laboratory applications and medical waste segregation.

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