Evaluating the Effectiveness of Image Resizing Algorithms for CNN-Based Fish Disease Classification

• 발행기관: 전남대학교 수산과학연구소
• 저자명: Dae-Hyon KIM
• 간행물 정보: 『수산과학연구소 논문집』제33권 제2호, 85~91쪽, 전체 7쪽
• 주제분류: 농수해양 > 수산학
• 파일형태: PDF
• 발행일자: 2024.12.31

국문 초록

The rapid growth of aquaculture has made the accurate diagnosis of fish diseases a critical challenge, with the potential for significant economic losses. This study evaluates the effectiveness of various image resizing algorithms in the context of convolutional neural network (CNN)-based fish disease classification. The performance of six resizing methods, including bilinear, Gaussian, and Hamming filters, applied in both one-step and two-step approaches, was examined across 30 experiments. The analysis focused on prediction accuracy, error rates, and variance. The results indicate that for bilinear and Gaussian algorithms, the two-step resizing methods generally outperformed the one-step approaches. In contrast, the Hamming resizing algorithm showed superior results with the one-step method. Notably, the one-step Hamming approach achieved the highest average prediction accuracy at 93.4% across 30 trials with varying random initializations. These findings suggest that selecting the appropriate image resizing technique is crucial for enhancing the accuracy of CNN-based models in fish disease classification.

목차

Introduction

Research Methodology

Research Results

Conclusions

References

키워드

Aquaculture Fish disease classification Convolutional Neural Networks (CNN) Image resizing algorithms Prediction accuracy