Fish Disease Detection Using Modified Haar Wavelet with Adaptive Coefficient Selection

White Spot Disease (WSD) is a major threat to aquaculture production and requires accurate image-based detection methods for early diagnosis. However, disease marker detection in fish images is challenging due to noise, illumination variations, low contrast, and complex background structures. This study proposes a Modified Haar Wavelet framework that integrates adaptive coefficient selection, Roberts operator-based edge enhancement, and hysteresis thresholding for White Spot Disease marker detection. The proposed framework aims to improve edge representation and disease marker localization while preserving disease-related structural information. Performance evaluation was conducted by comparing the proposed method against Otsu, Canny, Adaptive Thresholding, and conventional Haar Wavelet approaches using IoU, Precision, Recall, F1-score, PSNR, and SSIM. The proposed framework achieved the best overall performance with an IoU of 0.819, a precision of 0.849, a recall of 0.799, an F1-score of 0.823, a PSNR of 25.390 dB, and an SSIM of 0.889. Comparative analysis and ablation study further confirmed the effectiveness of adaptive coefficient selection, Roberts operator-based edge enhancement, and hysteresis thresholding. The results demonstrate that the proposed Modified Haar Wavelet framework provides an effective and robust solution for automated detection of White Spot Disease markers in aquaculture images.

@article{Handayani2026,
  title     = {Fish Disease Detection Using Modified Haar Wavelet with Adaptive Coefficient Selection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Tri Handayani and Nor Hazlyna Binti Harun},
  doi       = {10.14569/IJACSA.2026.0170658},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170658}
}