Paper Title
Fish Disease Detection System Using Deep Learning and Computer Vision

Abstract
This paper presents an intelligent fish disease detection system built using deep learning and computer vision techniques. A convolutional neural network (CNN) developed with TensorFlow and Keras is trained to classify seven major fish health conditions, covering bacterial, fungal, parasitic, and viral diseases. The system integrates a Flask-based web interface that allows users to upload images and receive real-time diagnostic results. Image preprocessing using OpenCV and NumPy ensures consistent input quality through resizing, normalization, and feature extraction. Experimental results show that the model achieves over 90% accuracy with response times below three seconds, making it suitable for practical aquaculture applications. The modular design further supports easy deployment, updates, and future system enhancements. Keywords - Fish disease detection, Deep learning, Convolutional neural network (CNN), Computer vision, Aquaculture management, TensorFlow, Keras, OpenCV, Image classification, Flask web application.