Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{179826,
        author = {N. BALASUBRAMANIAN and A. EPSEYBA},
        title = {FISH DISEASE DETECTION USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8428-8431},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179826},
        abstract = {Aquaculture plays an important role in 
global food production,and economic growth but it is 
highly vulnerable to the outbreak of diseases that can 
cause significant economic losses and ecological 
damage.Manual disease detection is labor-intensive, 
time-consuming, and often inaccurate results due to 
the subtlety of early symptoms. This project presents 
an automated fish disease detection system using 
(CNN) with Python and flask to identify and classify 
common fish diseases based on image data. The system 
enables users to upload fish images through a web
based interface, where the model analyzes visual 
symptoms such as lesions, discoloration, and abnormal 
growths.It 
accurately and efficiently identifies 
potential diseases and provides appropriatetreatment 
recommendations to the required user who interact 
with the system. This automated approach facilitates 
early disease detection, reduces fish mortality, and 
minimizes reliance on chemical treatments. The system 
is cost-effective, user-friendly, and scalable, offering an 
advanced technological solution to improve fish health 
monitoring and promote sustainable aquaculture 
practices.},
        keywords = {Fish Disease Detection, Convolutional  Neural Networks (CNNs), Aquaculture, Image  Analysis, Automated System.},
        month = {May},
        }