Image processing techniques for identification of fish disease

Malik, Shaveta; Kumar, T. Ganesh; Sahoo, Amiya Kumar

doi:10.1109/siprocess.2017.8124505

Cited by 52 publications

(30 citation statements)

References 15 publications

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“…The proposed system is tested using different types of fish diseases, especially Tail and Fin Rot, EUS, Red spot, Bacterial gill rot, Parasitic Disease (Argulus), Broken Antennae, and Rostrum. Finally, fish diseases are detected and recognized that can be prescribed in its early stages [4]. This research is designed in six sections.…”

Section: Introductionmentioning

confidence: 99%

Fish Disease Detection System: A Case Study of Freshwater Fishes of Bangladesh

Sikder¹,

Sarek²,

Das³

2021

IJACSA

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The proposed system is designed for automatic detection and classification of fish diseases in freshwater especially Rangamati Kaptai Lake and Sunamganj Hoar area of Bangladesh. Our experimental result is indicating that the proposed approach is significantly an accurate and automatic detection and recognition of fish diseases. This study presents fish disease detection based on the K-means and C-means fuzzy logic clustering method to segment the filtering image. Gabor's Filters and Gray Level Co-occurrence Matrix (GLCM) are used to extracts the features from the segmented regions. Finally Multi-Support Vector Machine (M-SVMs) is used for classification of the test image. The proposed system demonstrated a comparison between K-means clustering and C-means fuzzy logic. The proposed methodology gave 96.48% accuracy using K-means and 97.90% using C-means fuzzy logic which is the highest accuracy rate to compare other existing methods. The proposed system has been experimented in the MATLAB environment on infected fish images of Rangamati Kaptai Lake and Sumangan Hoar area. It is a challenging task of fisheries farming in Hoar areas and Lake areas to detect fish diseases initially. The proposed methodology can detect and classify different fish diseases in early stages and also contributes to improved results for fish disease detection.

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Section: Introductionmentioning

confidence: 99%

Fish Disease Detection System: A Case Study of Freshwater Fishes of Bangladesh

Sikder¹,

Sarek²,

Das³

2021

IJACSA

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“…The analysis refered to above is intended to enhance the inspection of the pathogen region in the image by means of object detection, which includes various techniques, including noise reduction, edge detection, morphological operations and context extraction. In addition, several neural network models have been used to contend with the animal sector, such as the support vector machine (SVM) [24,25], Single-Shot MultiBox Detector (SSD) model used to evaluate the percentage of reticulocytes in cat's samples [26], Alexnet for classification of fish disease [27]. The works described above makes it possible to apply deep learning algorithms in the field of veterinary medicine.…”

Section: Introductionmentioning

confidence: 99%

Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

Kittichai

Kaewthamasorn

Thanee

et al. 2021

Preprint

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Background: The infections of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to poultry industry because it cause economical loss in both quality and quantity of meat and egg productions. Deep learning algorithms have been developed to identify avian malaria infections and classify its blood stage development. Methods: In this study, four types of deep convolutional neural networks namely Darknet, Darknet19, darknet19_448x448 and Densenet 201 are used to classify P. gallinaceum blood stages. We randomly collected dataset of 10,548 single-cell images consisting of four parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. Results: In the model-wise comparison, the four neural network models gave us high values in the mean average precision at least 95%. Darknet can reproduce a superior performance in classification of the P. gallinaceum development stages across any other model architectures. In addition, Darknet also has best performance in multiple class-wise classification, scoring the average values of greater than 99% in accuracy, specificity, sensitivity, precision, and F1-score.Conclusions: Therefore, Darknet model is more suitable in the classification of P. gallinaceum blood stages than the other three models. The result may contribute us to develop the rapid screening tool for further assist non-expert in filed study where is lack of specific instrument for avian malaria diagnostic.

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“…Machine Learning algorithms do not apply to different feature Descriptors is a noticed problem herein. [10] In [11] CNN is used for the classification of the input animal images and also compares the overall recognition accuracy of the PCA, LDA, LBPH, and SVM. The proposed CNN was evaluated on the created animal database.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Machine Learning Approach for Canine Disease Detection Algorithm

2021

IJATCSE

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In recent times Machine Learning has been successfully utilized in diagnosing diseases for humans using various algorithms. In the account of diseases caused to animals like dogs, it can be implemented with analysis through developing diagnostic tools comprise of Big Data Analytics and Deep Learning algorithms. Human beings lacked information about the treatment of dogs, so this has to done through a veterinary framework and they provide traditional treatment of their pet dogs. Though life science is correlated with technology through a part of the neural network; a framework of diagnosis tool is designed with this phenomenon

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Image processing techniques for identification of fish disease

Cited by 52 publications

References 15 publications

Fish Disease Detection System: A Case Study of Freshwater Fishes of Bangladesh

Fish Disease Detection System: A Case Study of Freshwater Fishes of Bangladesh

Classification for Avian Malaria Parasite Plasmodium Gallinaceum Blood Stages by Using Deep Convolutional Neural Networks

A Survey on Machine Learning Approach for Canine Disease Detection Algorithm

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