## 4 Conclusions  
In this study, on the existing imbalanced dataset, deep learning models demonstrated higher performance in terms of evaluation metrics compared to canonical machine learning models trained with manually extracted features. Although canonical models may yield poor results, they can be beneficial when combined with deep learning models. Utilizing the ResNet50 model for automatic feature extraction and subsequent training of the RF algorithm with these extracted features led to a hybrid model achieving the highest performance in diseased sample detection. Since deep learning algorithms automatically extract features, classifying these features with RF resulted in high accuracy. The boosting and bagging methods used in RF contribute to this effect because, in RF, features are randomly selected when constructing trees, which helps reduce the correlation between trees. This finding serves as evidence for the usefulness of the canonical machine learning algorithms. Utilizing AdamW as the optimizer in the ConvNeXt-T deep learning algorithm led to higher performance compared to the models obtained by utilizing SGD. However, the utilization of the AdamW optimizer resulted in a disease detection performance sensitivity that was 1.3% lower than that of the hybrid model. According to [PERSON]'s test results, the classification performances of the hybrid model and the ConvNeXt-T model trained with AdamW are also statistically significant. Another noteworthy result is that the performance of the ResNet50 model, which provided the highest performance among the models trained with SGD for detecting diseased samples, was enhanced by 3.2% when utilizing the RF canonical machine learning algorithm. This study focuses solely on classification; the detection of diseased areas in images will be conducted in the next phase of the research. Future work will involve developing advanced image processing techniques to accurately identify and localize affected regions. This will enhance the overall diagnostic process and provide a more comprehensive understanding of the disease progression.  
Future research inspired by the findings of this study can focus on various directions. Firstly, enhancing the generalization capabilities of models by collecting larger and more balanced datasets is crucial. Investigating different organism species using similar models could expand biological monitoring capacity across ecosystems in terms of health and sustainability. Additionally, integrating the developed models with real-time data streams in field studies can facilitate rapid disease detection and control. Research efforts should prioritize optimizing deep learning models for more efficient operation. These endeavors could provide effective tools for monitoring and conserving the health of freshwater ecosystems, thereby contributing to environmental sustainability goals.  
Conceptualization, R.B. and S.B.; Methodology, K.A. and T.A.; Software, Y.A. and B.K.; Validation, M.S.G. and F.E.; Data Curation, R.B. and S.B.; Writing--Original Draft Preparation, K.A., T.A. and R.B.; Writing--Review and Editing, K.A., T.A., R.B., M.S.G. and F.E.; Visualization, K.A. and T.A.; Supervision, T.A., M.S.G., R.B. and S.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.