Early screening protozoan white spot fish disease using convolutional neural network

Md Azhar, Amiera Syazlin and Harun, Nor Hazlyna and Hassan, Mohamad Ghozali and Yusoff, Nooraini and Md Pauzi, Siti Naquiah and Yusuf, Nurul Nadiah and Kua, Beng Chu (2024) Early screening protozoan white spot fish disease using convolutional neural network. Journal of Advanced Research in Applied Sciences and Engineering Technology, 37 (1). pp. 49-55. ISSN 2462-1943

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Abstract

Aquaculture is in critical need of both intelligence and automation control in order to maintain a sustainable level of production. Historically, the accuracy of the disease diagnosis is determined by a person’s abilities, experiences and length of time spent. Due to the high level of expertise, time, and effort necessary to obtain an accurate diagnosis through manual inspection, inadequate early treatment could result in the rapid spread of the disease. As a result, there needs to be much focus on early-stage fish disease screening due to the rapid spread of infectious diseases in the vast fish system. This research focused specifically on Protozoan white spot disease, an infectious disease caused by Cryptocaryon irritans in saltwater considering the fact that the infection is contagious. Consequently, this research aims to create an intelligent system utilizing a convolutional neural network (CNN) algorithm, namely GoogleNet to detect infected fish based on raw underwater images taken. 90% accuracy achieved showed that the innovation could ease the process of fish disease screening. This effort could be a contributor to the aquaculture industry since humans rely on fish for survival in modern times for fisheries and livestock.

Item Type:	Article
Uncontrolled Keywords:	Aquaculture; Protozoan white spot disease; Underwater image; Disease screening; Convolutional neural network
Subjects:	Q Science > QA Mathematics > QA76 Computer software S Agriculture > SH Aquaculture. Fisheries. Angling T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	School of Computing
Depositing User:	Mr. Izhar Hj. Hashim
Date Deposited:	11 Nov 2026 04:35
Last Modified:	11 Nov 2026 04:35
URI:	https://repo.uum.edu.my/id/eprint/35075

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