Scopus Indexed Publications
Paper Details
- Title
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FishNet: Fish Classification using Convolutional Neural Network
- Author
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Shumaiya Akter Shammi,
Mehedi Hasan,
Sajal Das,
Sheak Rashed Haider Noori,
- Email
-
- Abstract
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Our main motive of this paper
is to classify fish for people. Because many of us don't know the class
of fish, name of fish. For that reason, we don't know the percentage of
vitamins and nutritions of a fish properly and cannot understand which
fish we should eat more. And this fish classification will also help the
people who work in marine fishing and it is very important to know the
classification of fish for them. Here we have identified six types of
fish using the traditional algorithm along with Convolutional Neural
Network (CNN) to classify fish. In traditional algorithms, we have
implemented some remarkable and performable Machine Learning Algorithms.
Convolutional Neural Network is considered as perfect and suitable for
classification. That's why we use CNN to classify fish on our model.
From various kinds of traditional algorithms, we got the best accuracy
from Support Vector Machine(SVM) where the accuracy is 63.93% and our
classification accuracy using Convolutional Neural Network is 88.96%.
- Keywords
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Computer Vision , Deep Learning , Image Processing , Machine Learning , Image Classification
- Journal or Conference Name
- 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)
- Publication Year
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2021
- Indexing
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scopus