Convolutional Neural Network Architectures Performance Evaluation for Fish Species Classification

Abstract

Fish image classification tool is important in the field of ichthyology. In this paper, we present a fish image classification benchmark comparison across different types of convolutional neural network (CNN). CNN extracts features from labeled image data to solve classification problems. CNN models were trained to classify fish images using transfer learning with data augmentation. CNN models consisting of AlexNet, GoogLeNet, and ResNet were incorporated in the benchmark tests. A dataset of 18,000 fish images across 18 categores, were split into 5,400 images for validation (30%) and 12,600 images for training (70%) dataset. Such neural network models show high accuracy up to 99.85% (AlexNet), 96.39% (GoogLeNet) and 99.51% (ResNet-50). To evaluate the performance of each framework, the analysis presented consists of classification accuracy, learning curve, validation test, top-five prediction and confusion matrix. The work presented here has shown its potential to contribute towards accurate development of state-of-the-art fish classification tools. It is envisioned that these CNN algorithms have the potential to assist in fish image classification problems with high accuracy despite visually similar features of images in the dataset.

Fish Species Identification Techniques: A Review

“…Early techniques for fish types identification were carried out in controlled circumference only. Most of the researchers attempt to identify the fish images based on: the off-line fish images, natural environmental conditions and databases in [15,21,23,25,28]. So, there was no need to use image prefiltering.…”

“…[21] Genetic algorithm with tabu search and a back-propagation algorithm 82.1% for back-propagation, 87% for GTB [23] Ma-b algorithm and back propagation algorithm 90% for MA-B algorithm 82.25% for back propagation algorithm [24] Deep convolutional neural networks 80.58% [25] Multi-level residual network 99.69% [28] Neural network Not reported [32] Convolutional neural networks 96.55%.…”

“…[25] 100 8 species -Fish-gres database -Fish4-knowledge database [28] 18,000 Most of researches based on methods that classified fishes outside of water, some of researches depend on video datasets that created in the unrestrained underwater environment [32,34]. An extra effort needs to be made on the underwater fish classification to confront the turbidity of water and the confusion of background.…”

Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture

“…Here, we propose to use ResNet-9 architecture for building model CNN to classify disease corn plants. residual networks (ResNet) are a convolutional network that is trained on more than 1 million images from the ImageNet database [28]- [30] and ResNet-9 has a pretrained network that can classify images of up to 1,000 object categories which makes the network used to learn good feature representation for various images. The main contribution in this study can be highlighted as: i) designing of new CNN model for diseases classification on corn plants; ii) doing comparisons of epochs with the distribution of 80% training data and 20% testing data have been carried out to obtain the best model; iii) using ResNet-9 architecture and Adam optimizer to train the best model; and iv) building a web interface that can classify diseases in corn plants which will display the label and the accuracy of classification results displayed.…”