A comprehensive analysis of feature ranking-based fish disease recognition

In recent years, the field of emerging computer vision systems has witnessed significant advancements in automated disease diagnosis through the utilization of vision-oriented technology. This article proposes an optimal approach for detecting the presence of ailments in Rohu fish. The aims of our research is to identify the most significant features based on Analysis of Variance (ANOVA) feature selection and evaluate the best performance among all features for Rohu fish disease recognition. At the outset, diverse techniques for image preprocessing were employed on the acquired images. The region affected by the disease was partitioned through utilization of the K-means clustering algorithm. Subsequently, 10 distinct statistical and Gray-Level Co-occurrence Matrix (GLCM) features were extracted after the image segmentation. The ANOVA feature selection technique was employed to prioritize the most significant features N (where 5 $\le$ N $\le$ 10) from the pool of 10 categories. The Synthetic Minority Oversampling Technique, often known as SMOTE, was applied to solve class imbalance problem. After conducting training and testing on nine different machine learning (ML) classifiers, an evaluation was performed to estimate the performance of each classifier using eight various performance metrics. Additionally, a receiver operating characteristic (ROC) curve was generated. The classifier that utilized the Enable Hist Gradient Boosting algorithm and selected the top 9 features demonstrated superior performance compared to the other eight models, achieving the highest accuracy rate of 88.81%. In conclusion, we have demonstrated that the feature selection process reduces the computational cost.