Source of Publication
International Journal of Electrical and Computer Engineering
In computer vision, image classiﬁcation is one of the potential image processing tasks. Nowadays, ﬁsh classiﬁcation is a wide considered issue within the areas of machine learning and image segmentation. Moreover, it has been extended to a variety of domains, such as marketing strategies. This paper presents an effective ﬁsh classiﬁcation method based on convolutional neural networks (CNNs). The experiments were conducted on the new dataset of Bangladesh’s indigenous ﬁsh species with three kinds of splitting: 80-20%, 75-25%, and 70-30%. We provide a comprehensive comparison of several popular optimizers of CNN. In total, we perform a comparative analysis of 5 different state-of-the-art gradient descent-based optimizers, namely Adaptive Delta (AdaDelta), Stochastic Gradient Descent (SGD), Adaptive Momentum (Adam), Adaptive Max Pooling (Adamax), Root Mean Square Propagation (Rmsprop), for CNN. Overall, the obtained experimental results show that Rmsprop, Adam, Adamax performed well compared to the other optimization techniques used, while AdaDelta and SGD performed the worst. Furthermore, the experimental results demonstrated that Adam optimizer attained the best results in performance measures for 70-30% and 80-20% splitting experiments, while the Rmsprop optimizer attained the best results in terms of performance measures of 70-25% splitting experiments. Finally, the proposed model is then compared with state-of-the-art deep CNNs models. Therefore, the proposed model attained the best accuracy of 98.46% in enhancing the CNN ability in classiﬁcation, among others.
Adam, BDIndigenousFish201, CNNs, Deep learning, Features extraction, Fish classification, Optimizers
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Al Smadi, Ahmad; Mehmood, Atif; Abugabah, Ahed; Almekhlaﬁ, Eiad; and Al-smadi, Ahmad Mohammad, "Deep convolutional neural network-based system for ﬁsh classiﬁcation" (2022). All Works. 4718.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series