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 r...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-03-01
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| Series: | Array |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005623000541 |
| _version_ | 1797266463198806016 |
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| author | Aditya Rajbongshi Rashiduzzaman Shakil Bonna Akter Munira Akter Lata Md. Mahbubul Alam Joarder |
| author_facet | Aditya Rajbongshi Rashiduzzaman Shakil Bonna Akter Munira Akter Lata Md. Mahbubul Alam Joarder |
| author_sort | Aditya Rajbongshi |
| collection | DOAJ |
| description | 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 ≤ N ≤ 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. |
| first_indexed | 2024-03-09T02:02:17Z |
| format | Article |
| id | doaj.art-e7322a09065a49e28e2363ba37577871 |
| institution | Directory Open Access Journal |
| issn | 2590-0056 |
| language | English |
| last_indexed | 2024-04-25T01:01:05Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj.art-e7322a09065a49e28e2363ba375778712024-03-11T04:11:03ZengElsevierArray2590-00562024-03-0121100329A comprehensive analysis of feature ranking-based fish disease recognitionAditya Rajbongshi0Rashiduzzaman Shakil1Bonna Akter2Munira Akter Lata3Md. Mahbubul Alam Joarder4Bangabandhu Sheikh Mujibur Rahman Digital University, BangladeshDaffodil International University, Bangladesh; Corresponding author.Daffodil International University, BangladeshBangabandhu Sheikh Mujibur Rahman Digital University, BangladeshUniversity of Dhaka, BangladeshIn 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 ≤ N ≤ 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.http://www.sciencedirect.com/science/article/pii/S2590005623000541Fish diseaseGLCMSMOTEFeature selectionANOVAMachine learning |
| spellingShingle | Aditya Rajbongshi Rashiduzzaman Shakil Bonna Akter Munira Akter Lata Md. Mahbubul Alam Joarder A comprehensive analysis of feature ranking-based fish disease recognition Array Fish disease GLCM SMOTE Feature selection ANOVA Machine learning |
| title | A comprehensive analysis of feature ranking-based fish disease recognition |
| title_full | A comprehensive analysis of feature ranking-based fish disease recognition |
| title_fullStr | A comprehensive analysis of feature ranking-based fish disease recognition |
| title_full_unstemmed | A comprehensive analysis of feature ranking-based fish disease recognition |
| title_short | A comprehensive analysis of feature ranking-based fish disease recognition |
| title_sort | comprehensive analysis of feature ranking based fish disease recognition |
| topic | Fish disease GLCM SMOTE Feature selection ANOVA Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2590005623000541 |
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