Fish Species Classification Using Probabilistic Neural Network

Andayani, U; Wijaya, Alex; Rahmat, R F; Siregar, Baihaqi; Syahputra, M F

doi:10.1088/1742-6596/1235/1/012094

Cited by 17 publications

(9 citation statements)

References 2 publications

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“…Research by Andayani, et al (2018) used three types of fish in the Scombridae family which were classified using the Neural Probabilistic Network method with an accuracy rate of 89.65% using 112 training data images and 29 image data testing [11].…”

Section: Methodsmentioning

confidence: 99%

Classification of Fish Species with Image Data Using K-Nearest Neighbor

Kaharuddin

Sholeha

2021

IJCIS

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Abstract— Classification is a technique that many of us encounter in everyday life, classification science is also growing and being applied to various types of data and cases in everyday life, in computer science classification has been developed to facilitate human work, one example of its application is to classify fish species in the world, the number of fish species in the world is very much so that there are still many people who are sometimes confused to distinguish them, therefore in this study a study will be conducted to classify fish species using the K-Nearest Neighbor Method. 4 types of fish, all data totaling 160 data. The purpose of this study was to test the K-Nearest Neighbor method for classifying fish species based on color, texture, and shape features. Based on the test results, the accuracy value of the truth is obtained using the value of K = 7 with a percentage of the truth of 77.50%, the second-highest accuracy value is the value of K = 10, namely 76.88%. Based on the results of this study, it can be concluded that the K-Nearest Neighbor method has a good enough ability to classify, but it can be done by adding variables or adding more amount of data, and using other types of fish.

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Section: Methodsmentioning

confidence: 99%

Classification of Fish Species with Image Data Using K-Nearest Neighbor

Kaharuddin

Sholeha

2021

IJCIS

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“…Liang et al [3] 2020 Shape features Convolutional neural network 98.1% Knausgård et al [4] 2022 Generic features Convolutional neural network 87.74% Böer et al [5] 2021 Morphological features DeepLabV3 and PSPNet models 96.8% Iqbal et al [6] 2019 Generic features AlexNet model 90.48% Cui et al [7] 2020 Generic features Convolutional neural network 97.5% Zhang et al [8] 2021 Morphological features Convolutional neural network 96% Montalbo and Hernandez [9] 2019 Generic features VGG16 DCNN Model 99% Mathur and Goel [10] 2021 Generic features ResNet-50 Model 98.44% Ahmed et al [11] 2022 Statistical & color features SVM classifier 94.12% Lan et al [12] 2020 Shape and texture features Deep CNN 89% Allken et al [13] 2018 Shape features A deep learning neural network 94% Andayani et al [14] 2019 In this paper, RBFNN and SVM techniques for fish image classification were presented and evaluated against the fish shape features. The contribution of this work can be summarized as: -Extracted robust features for fish image classification.…”

Section: 6%mentioning

confidence: 99%

“…In this work, a novel training regime is developed to cue the scarcity of training data and achieved a classification rate of 94%. Andayani et al [14] used a combination of geometric invariant moment, gray-Level co-occurrence matrix (GLCM), and hue saturation value (HSV) feature extraction methods to extract fish images features and for fish species classification purposes, they used probabilistic neural network (PNN) method utilized to properly classify fish species and achieved 89.65% accuracy rate. Others utilize convolutional neural networks (CNN) using deep learning for fish classification [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Robust features extraction from shape signature for fish images classification

Ahmed

Hussein

Gali

2023

IJEECS

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Recently, the process of fish species classification has become one of the most challenging problems addressed by researchers. In this work, a robust scheme to classify fish images based on robust feature extraction from shape signatures is proposed. First, the image contour is fitted using one of the common approaches named radial basis function neural network (RBFNN) fitting to obtain image centroid. Afterward, prominent features from the shape signature are extracted. These features are representative of fish shapes because they can distinguish the characteristics of each class as well as being relatively robust to scale and rotation changes. Finally, for the classification process purpose, RBFNN is used again for image classification against one of the most commonly used classification techniques called support vector machine (SVM). The proposed paradigm has been applied to a standard fish dataset acquired from a live video dataset grouped into twenty-three clusters representing specific fish species. The resulting accuracy based on SVM and RBFNN was 90.41% and 98.04%, respectively.

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“…The results showed that it could classify poisonous or non-poisonous fishes. In addition, research on fish classification has also been carried out using an image processing approach and an artificial neural network to classify fish species effectively and efficiently using a Probabilistic Neural Network with an 89.65% classification accuracy [12].…”

Section: Introductionmentioning

confidence: 99%

Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network

Latumakulita

Astawa

Mairi

et al. 2022

JOIV : Int. J. Inform. Visualization

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Feature extraction is important to obtain information in digital images, where feature extraction results are used in the classification process. The success of a study to classify digital images is highly dependent on the selection of the feature extraction method used, from several studies providing a combination of feature extraction solutions to produce a more accurate classification. Classifying the types of marine fish is done by identifying fish based on special characteristics, and it can be through a description of the shape, fish body pattern, color, or other characteristics. This study aimed to classify coral reef fish species based on the characteristics contained in fish images using Backpropagation Neural Network (BPNN) method. Data used in this research was collected directly from Bunaken National Marine Park (BNMP) in Indonesia. The first stage was to extract shape features using the Geometric Invariant Moment (GIM) method, texture features using Gray Level Co-occurrence Matrix (GLCM) method, and color feature extraction using Hue Saturation Value (HSV) method. The third value of feature extraction was used as input for the next stage, namely the classification process using the BPNN method. The test results using 5-fold cross-validation found that the lowest test accuracy was 85%, the highest was 100%, and the average was 96%. This means that the intelligent model derived from the combination of the three feature extraction methods implemented in the BPNN training algorithm is very good for classifying coral reef fish.

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Fish Species Classification Using Probabilistic Neural Network

Cited by 17 publications

References 2 publications

Classification of Fish Species with Image Data Using K-Nearest Neighbor

Classification of Fish Species with Image Data Using K-Nearest Neighbor

Robust features extraction from shape signature for fish images classification

Combination of Feature Extractions for Classification of Coral Reef Fish Types Using Backpropagation Neural Network

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