# Collaborative CNN with Multiple Tuning for Automated Coral Reef Classification

R. Jannathul Firdous, S. Sabena

► To cite this version:

R. Jannathul Firdous, S. Sabena. Collaborative CNN with Multiple Tuning for Automated Coral Reef Classification. 7th International Conference on Computer, Communication, and Signal Processing (ICCCSP), Jan 2023, Chennai, India. pp.81-93, 10.1007/978-3-031-39811-7_7. hal-05363228

HAL Id: hal-05363228

https://inria.hal.science/hal-05363228v1

Submitted on 13 Nov 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
<figure><img src="image_3.png" /></figure>

# Collaborative CNN with Multiple Tuning for Automated Coral Reef Classification

R. Jannathul Firdous<fnref n="1" /> and S. Sabena<fnref n="2" />✉

**Abstract.** The earliest and oldest ecosystems on our planet are found in coral reefs. The first step in protecting marine life is to have an understanding of the locations of coral reefs. In recent years, there has been a significant proliferation of digital imagery that is utilized for monitoring marine species. Because of the exponential development in the amount of image data that is now accessible, automated detection and classification methods are becoming more necessary. Only around two percent of the images that are collected can be personally examined by an expert in marine biology. (i) The resemblance between various species of coral and (ii) the difficulty in determining the geographical borders between classes due to the fact that many corals occur in groups. These are two of the problems that arise while attempting to identify coral. The purpose of this article is to discuss the findings of research conducted on a completely automated system for the accurate identification and categorization of coral reefs found in the ocean. The solution that is being proposed here is an ensemble of two different Convolutional Neural Network (CNN) models that include fully linked layers that can be adjusted, a variable number of neurons, and training data. When it comes to fine-tuning a pre-trained CNN, our primary emphasis is on the multiple tuning technique. We have shown that our modified pre-trained network can be applied to two datasets, RSMAS and EILAT, and that it can deliver results with high accuracy that are on par with the techniques that are considered to be state-of-the-art in this field.

**Keywords:** Convolutional Neural Network (CNN) · Ensemble · Automatic Detection · Fully connected layers · Pre trained network

## 1 Introduction

Coral reefs, which are found in the warm, shallow waters of the tropics, are one of the most complex marine ecosystems today. As one species of hard coral dies, its calcium carbonate skeleton is left behind for another to colonize and build the reef. Coral reefs, due to their high levels of biodiversity, are often regarded as the most important ecosystems on Earth. They aid in water purification by absorbing excess nitrogen and carbon, provide a resource for medical research, and generate income via fishing and tourism;

<footnote n="1">¹ A. R College of Engineering and Technology, Tirunelveli, Tamil Nadu, India</footnote>

<footnote n="2">² Anna University Regional Campus, Tirunelveli, Tamil Nadu, India
sabenazulficker@gmail.com</footnote>
certain coral species even act as a natural barrier to protect coastlines from hurricanes and other severe weather [1]. A quarter of all marine life on Earth and as many as two million species depend on them for survival. From a human perspective, they are also crucial [2]. Because of this, the identification and categorization of coral reefs have been receiving more and more focus from both private companies and government organizations. This difficulty stems from a number of features that together make it hard to categorize corals:

* Additionally, certain coral species have varying sizes, forms, and colors, while other coral species may seem to be visually similar to the human eye.

* Images vary in terms of illumination, visibility, depth, and the presence of soft and hard corals in the foreground and background.

* Thus, having a qualified biologist has always been essential for accurate coral categorization. It would be fantastic if we could automate the categorization of the vast quantities of coral photos that are now being gathered. The technique discussed in this research may be used to further refine the classification of coral species.

Our key contribution is a new method of tuning a network that allows us to generate CNN ensembles for classification much more quickly.

The goal is to build a classification network, often known as the “body” of a network, from a CNN that is only coarsely optimized but serves as a solid foundation for future refinement. Afterward, the obtained networks are tweaked and fine-tuned many times using new data. A major aspect in ensemble performance is the variety of the classifiers, thus we take care to preserve it. Training time was reduced by around half while the ensemble’s classification performance was maintained with this method.

## 2 Related Works

A small number of publications have explored the possibility of automatically classifying coral photos. This article explains it in depth. The authors of referenced work [6] retrieved color and texture features using the L*a*b* color space and a Maximum Response (MR) filter bank, then employed a support vector machine (SVM) classifier. This method may automatically annotate an appropriate picture patch for species identification with little effort, and it can classify images based on color data; however, this information is often inconsistent or nonexistent in underwater datasets. For colors and textures, he implemented quantile functions (QF) and Scale-Invariant Feature Transform (SIFT) in [8], and then utilized Linear support vector machine to analyze the results (SVM). Its automated segmentation of features allows it to succeed when other ML techniques have failed. Although SIFT is a fast feature descriptor, it is still time-consuming.

Local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, opponent angle, hue channel color histograms, and k- nearest neighbor (KNN), neural network (NN), support vector machine (SVM), and probability density weighted mean distance (PDWMD) were all used in the study described in [7]. Very excellent results are achieved when compared to other texture datasets using the same features and classifiers. When classifying a new picture, it is necessary to utilize a large number of methods until the optimal combination is found. It takes a lot of time. The studies
[9, 10] utilized LDP, Tilted Z Local Binary Pattern, and KNN, all of which produced high accuracy, but none of which used deep learning for feature extraction and coral categorization.

The use of CNNs presents difficulties in coral classification because of differences in picture quality even within the same class, lighting fluctuations caused by the water column, and the tendency of some coral species to appear in groups. Not only that, but a high-quality performance from a CNN requires a large training dataset. Two common methods used in practice are transfer learning and data augmentation to get over this restriction. Some publications make use of CNNs for the purpose of coral categorization. Researchers in [1] initially used CNNs to the task of classifying coral. Images with texture and form details were given a color boost and smoothed out using filters. He then trained a model using the LeNet-5 neural network and achieved 55% accuracy. Using VGGnet, [4] was able to achieve 90% accuracy on the BENTHOZ-2015 dataset. In [5], the MLC dataset was combined with manually constructed features. Extraction of features using CNNs was performed using the VGGnet network, which had been pre-trained on ImageNet, and achieved an accuracy of 84.5% in the MLC. Anima Brown Octa-angled Pattern for Triangular sub area (OPT) Pulse Coupled Convolutional Neural Network (PCCNN) was used by Mary N [11]. By using the PCCNN to filter away unnecessary characteristics from CNNs' output, we were able to achieve remarkable improvements.

While these studies analyze widely-used CNNs like VGGnet and LeNet, they only test their models on a single dataset in total [3]. In addition, the photos do not provide any information on the complete body of the corals; the EILAT and RSMAS datasets, in particular, are especially fascinating because to their tiny sizes, high imbalance, and inclusion of small areas of the corals' textures.

In order to circumvent the shortcomings of currently used deep learning models, we suggest using more robust CNNs. In order to solve the unique issues involved in coral classification, we want to use many data sets to train a model that is as accurate as a human expert. Specifically, we have thought of a combination of two of the most promising CNNs, ResNet [12] and DenseNet [13].

An explanation of the work's significance was illustrated below,

1) The first stage is to optimize a CNN that has already been trained for the categorization of coral reefs.

2) Here, we use a compound tuning method, in which we first fine-tune on a dataset that is analogous to the target problem's training set, and then we fine-tune again using data from the actual problem's training set.

3) And thirdly, it's distinct in other ways as well. Both of the candidate CNN networks employ fully linked layers for making the final decision.

4) After that, we use a streamlined method of generating ensembles of CNNs by combining a DenseNet 121 and ResNet 50 variant for the classification of small datasets of underwater coral texture photos.

5) Contrast our findings to those obtained by using the most advanced but labor-intensive classical techniques
## 3 CNN Architectures

The ResNet (ResNet-50) [12] and DenseNet (DenseNet-161) [13] CNN architectures employed here were selected because they provide state-of-the-art results in distinct classification challenges while also being complimentary to one another. A ResNet element is defined in the same way as in [12]:

$$y_g = R_g(y_{g-1}) + y_{g-1} \quad (1)$$

where x0 is the starting point and $y_{g-1}$, and $y_g$ are the input and output of layer g. Let R() be a non-linear transformation, consisting of a combination of operations like convolution, relu, pooling, batch normalization, and so on, applied to layer. By stacking many of these units, ResNets are able to create gradient bypasses that enhance the efficiency of back-propagation optimization in very deep networks. Thus, ResNets may produce unnecessary layers. DenseNet units were suggested in [24] as an alternative method for enhancing the gradient flow in deep networks. This would include connecting each layer to the layers above it.

$$y_g = R_g([y_0; y_1; \dots; y_{g-1}]) \quad (2)$$

where $[y_0; y_1; \dots; y_{g-1}]$ to the outputs of the preceding layers and appends them together. DenseNets are similar in that they stack several units, but they employ transition layers to regulate the amount of connections between them. Finally, Dense Nets make up for their lack of parameters by reusing features, making them competitive with ResNets. That they act differently might be a contributing factor to their complementary relationship. We use ImageNet [27] weights to seed both the Dense Net and ResNet models. We have taken off the last layer in each network, which is responsible for assigning photos to one of the ImageNet categories. After that, we've included a ReLU activation layer, a layer of distinct neurons, and a final layer with as many neurons as classes in the dataset, all of which are completely linked. Since we are only working with limited datasets, we only train the fully linked layers and have frozen the rest. As an optimizer, we deployed Stochastic Gradient Descent with a 0.001-percent learning rate, a 10-percent decay rate, and an overall learning rate of 1.

Multiple tuning is conducted in the two selected networks with different fully connected layers and different number of neurons and then ensemble to provide very good results. Initially the datasets EILAT, RSMAS are too small for the deep architectures to train and data augmentation is needed to overcome such limitation. Here we go for a novel approach of creating a new wider datasets by combining images with large set of classes from EILAT, RSMAS, EILAT 2 and create another deeper dataset which consists of large number of images with less number of classes from combining EILAT, RSMAS, EILAT 2. Our CNN architecture such as Resnet-50 and Dense net 121 with difference in their architecture are trained by above mentioned datasets. Of these two architectures, Resnet-50 is less deeper than Densenet as Resnet-50 consists of 50 layers and Densenet-121 consists of 121 layers so another criteria is to be noted i.e. we can supply wider dataset to the thin network i.e. (Resnet-50) and larger dataset for thick network (i.e. Dense net-121) which has 121 layers.
Specifically, we want to implement a fully connected layer as the very last layer in both CNN topologies, just before the softmax layer. These layers are just a fully connected layer with the same number of neurons as classes in the dataset, followed by a softmax activation layer. For the sake of our shortcut, we will refer to the Resnet-50 as the $M_1$ model and the Densenet121 as the $M_2$ model.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Pictorial representation of the suggested methodology</figcaption></figure>
The overall representation of the suggested architecture was illustrated in Fig. 1

## 3.1 M₁ Model

<table><caption>**Table 1. Layers Description of Resnet-50**</caption><thead><tr><th>Layers</th><th>Size</th><th>ResNet-50</th></tr></thead><tbody><tr><td>Conv 1</td><td>112 x 112</td><td>7 x 7,64, stride 2</td></tr><tr><td>Conv 2</td><td>56 x 56</td><td>3 x 3 max pool, stride 2<br>[[1 x 1, 64]<br>3 x 3, 64]<br>1 x 1, 256]<br>× 3</td></tr><tr><td>Conv 3</td><td>28 x 28</td><td>[[1 x 1, 128]<br>3 x 3, 128]<br>1 x 1, 512]<br>× 4</td></tr><tr><td>Conv 4</td><td>14 x 14</td><td>[[1 x 1, 256]<br>3 x 3, 256]<br>1 x 1, 1024]<br>× 6</td></tr><tr><td>Conv 5</td><td>7 x 7</td><td>[[1 x 1, 512]<br>3 x 3, 512]<br>1 x 1, 2048]<br>× 3</td></tr><tr><td>Classification Layer</td><td>1 x 1</td><td>Global avg. Pool, no.of class,- fc, softmax</td></tr></tbody></table>

M₁ CNN uses a 224 x 224 pixel input picture as its standard. Table 1 displays the architecture of the M₁ model, which includes an explanation of the filter sizes and layer outputs. Each unit is repeated three times for a total of three, four, six, and three times. The completely linked 1000-neuron layer is designated as FC 1000. The year 2048 is a symbol for very strong residual traits (DRF). In our coral reef classification issue, we state that the DRF and the number of neurons in the final FC layer change as a function of the classes of coral dataset. Due to the nature of the M₁ model, the network is rather small and isolated. As described in [14], for broader datasets we need a larger number of neurons in FC layers and a larger total number of FC layers. In accordance with [14], we add two FC layers at the beginning, and the number of neurons is selected (for freshly added FC layer) as a power of two more than the number of classes, up to 4096. After that, we improve the model by adding a further FC layer, as described in subsection v.

## 3.2 M₂ Model

Our model M₂ is Densenet-121 architecture which consists of 121 layers.
<table><caption>**Table 2.** Layers Description of DenseNet-121</caption><thead><tr><th>"Layers"</th><th>"Size"</th><th>"DenseNet-121"</th></tr></thead><tbody><tr><td>"Convolution</td><td>112 × 112</td><td></td></tr><tr><td>"Pooling</td><td>56 × 56</td><td></td></tr><tr><td>Dense Block (1)</td><td>56 × 56</td><td>[1 × 1<sup>conv</sup><br>3 × 3<sup>conv</sup>]<br>× 6</td></tr><tr><td>Transition Layer (1)</td><td>56 × 56<br>28 × 28</td><td></td></tr><tr><td>Dense Block (2)</td><td>28 × 28</td><td>[1 × 1<sup>conv</sup><br>3 × 3<sup>conv</sup>]<br>× 12</td></tr><tr><td>Transition Layer (2)</td><td>28 × 28<br>14 × 14</td><td></td></tr><tr><td>Dense Block (3)</td><td>14 × 14</td><td>[1 × 1<sup>conv</sup><br>3 × 3<sup>conv</sup>]<br>× 24</td></tr><tr><td>Transition Layer (3)</td><td>14 × 14<br>7 × 7</td><td></td></tr><tr><td>Dense Block (4)</td><td>7 × 7</td><td>[1 × 1<sup>conv</sup><br>3 × 3<sup>conv</sup>]<br>× 24"</td></tr><tr><td>Classification Layer</td><td>No. of classes</td><td></td></tr></tbody></table>

Table 2 shows that DenseNet's foundation is a convolution and pooling layer. Next comes a dense block, then a transition layer, then another dense block, then another transition layer, and lastly a dense block, then a classification layer. The table lists details about the layers, filter size, and repetition units. Thicker model networks, such as the one described in [14], such as the one used here, need a less number of neurons in the FC layers. Similar to the findings in [14], the performance of deeper CNNs is superior to that of shallower models when applied to larger datasets. In this model, we employ a smaller number of FC layers to accommodate deeper datasets. Thus, in the end, we only add two or one completely connected layer, which results in a much less number of neurons but a larger total than the number of classes in our model $M_2$.

## 4 Ensemble Method of Averaging

To boost the efficiency of the models, ensemble learning techniques have used general methods, classical methods, and other fusion approaches. Given the computational and data demands of deep learning models, it is essential to pay close attention to ensemble deep learning models as they integrate the mutually beneficial insights of numerous algorithms into a unified whole. In our case, we combine the results of the two best models
by utilizing the unweighted average ensemble technique [15]. According to [15], because to the large variance and low bias of Deep Learning architectures, the generalization performance may be improved by simply averaging the ensemble models. Either the base learners' outputs themselves are averaged, or the classes' predicted probabilities are averaged using the softmax function.

$$G_k^i = \frac{P_k^i}{\sum_{l=1}^{m} \exp(P_k^i)} \quad (3)$$

Where m is the number of classes, the output of the kth unit of the i th base learner (P<sub>k</sub>), and the probability of the k th unit's output (G<sub>k</sub>). When the performance of the base learners is similar, unweighted averaging is a good choice.

# 5 Experiment and Analysis

## 5.1 Dataset Used

EILAT is a collection of corals that consists of 973 RGB picture patches that are 64 × 64 pixels in size. The patches were extracted from bigger images that were taken at coral reefs located in the vicinity of Eilat in the Red Sea. The dataset is broken up into eight different categories, each of which has an uneven distribution.

The RSMAS dataset is a compact one, with just 766 RGB picture patches (256 × 256 pixels) representing coral.

The patches are from the University of Miami's Rosenstiel School of Marine and Atmospheric Sciences and are derived from bigger pictures. Various cameras were used at various locations to get these photographs. It should be noted that the dataset is split into 14 unequal groups.

The 128x128-pixel EILAT2 dataset is a subset of the whole EILAT dataset. It contains 304 picture patches of five distinct kinds.

<table><caption>**Table 3.** Dataset Description</caption><thead><tr><td>Dataset</td><td>No. of images</td><td>No. of classes</td><td>Type</td></tr></thead><tbody><tr><td>“EILAT + RSMAS + EILAT2</td><td>973 + 322 + 103 = 1398</td><td>13</td><td>Deeper (Set 1)</td></tr><tr><td>RSMAS + EILAT + EILAT2</td><td>766 + 303 + 50 = 1119</td><td>26</td><td>Wider (Set 2)</td></tr><tr><td>EILAT2 + RSMAS + EILAT”</td><td>304 + 677 + 798 = 1779</td><td>27</td><td>Deeper & wider (Set 3)</td></tr></tbody></table>

The dataset in Table 3 is obtained by selecting particular number of images from each dataset and form a new dataset which should be under the category of deeper, wider. We also form a dataset of both the type. Deeper dataset means the one with large number of images and the wider means one with large number of classes. We proceed our experiment with these datasets using our modified CNN architectures. Our performance analysis and other metrics are explained below.
## 5.2 Performance Analysis

Through our experiments, we were able to enhance the precision and efficiency of underwater picture categorization challenges. One the one hand, there are numerous groups of underwater pictures that feature examples that are drastically different from one another in terms of geometry, color, texture, size, orientation, brightness, depth of field, and so on. However, there are overlapping groups that seem almost identical when seen from certain perspectives and distances. The issues of what patch size is best and how much to change patches remain unanswered. Further, other difficulties such blurring from motion, color attenuation, refracted sunlight patterns, water temperature volatility, sky color variation, and scattering effects must be mitigated to preserve picture quality and trustworthy information content. These concerns are highlighted, and a solution to them is given in the form of an ensemble technique.

Finding patterns in images and then exploiting those patterns for categorization is a particularly strong suit of deep learning algorithms, and in particular convolutional neural networks (CNNs). In typical CNN fashion, the algorithm is applied to a patch of an image and the patch is then given a class weight. The picture tiles that will be sent to Resnet 50. We have only trained fully connected layers since we have utilized transfer learning to initialize the considered CNNs with the pre-trained weights of the networks on ImageNet and we have frozen all the layers except for the addition of fully connected levels. Resnet overfits when trained on insufficient data, thus to prevent this we merge the EILAT and RSMAS datasets, paying special attention to class imbalance as we do so. We get around this by dividing the total number of photos into three equal halves and using it for our multiple training. Accuracy is used as a measure of our method's effectiveness. The exact same process is performed while making the dense net 121.

### Overall Accuracy (OA)

The Overall Accuracy (OA) of a collection of categorized data is calculated as the percentage of test pictures that were properly labeled.

$$ OA = \frac{\text{Number of images classified correctly}}{\text{Total images in dataset}} \quad (4) $$

## 5.3 Modified CNN Architectures

### 5.3.1 Resnet 50

The input shape in M1 Model is originally 224 × 224 × 3, but we've shortened it to 180 × 180 × 3 for more manageable input. Grid search is used to determine the optimal values for the hyperparameters, and the results are analysed in terms of performance. The settings used for this analysis are as follows: learning rate = 0.001, batch size = 64, and the number of epochs is set at 50. We have compiled a comprehensive table detailing results across all available data sets.
<table><caption>**Table 4.** Epoch = 50 Accuracy is tabulated below for the type of wider dataset of set 2 by increasing the number of neurons.</caption><thead><tr><th>One Fully Connected</th><th>Two Fully Connected</th><th>Three Fully Connected</th></tr></thead><tbody><tr><td>32 x 27(88.45)</td><td>(64,32) x 27 (91.23)</td><td>(128,64,32) x 27(95.6)</td></tr><tr><td>64 x 27 (88.93)</td><td>(128,64) x 27 (91.72)</td><td>(256,128,64) x 27(96.34)</td></tr><tr><td>128 x 27(88.98)</td><td>(256,64) x 27(91.26)</td><td><b>(512,256,128) x 27(96.78)</b></td></tr><tr><td>256 x 27(89,11)</td><td>(512,64) x 27(92.49)</td><td>(1024,512,256) x 27(96.76)</td></tr><tr><td>512 x 27(89.39)</td><td><b>(1024,64) x 27(93.72)</b></td><td></td></tr><tr><td><b>1024 x 27(89.99)</b></td><td></td><td></td></tr></tbody></table>

<table><caption>**Table 5.** Epoch = 50 Accuracy is tabulated below for the deeper dataset of set 1 by increasing the number of neurons.</caption><thead><tr><th>One Fully Connected</th><th>Two Fully Connected</th><th>Three Fully Connected</th></tr></thead><tbody><tr><td>32 x 26(87.880)</td><td>(64,32) x 26 (91.780)</td><td>(128,64,32) x 26(94.890)</td></tr><tr><td>64 x 26 (87.720)</td><td>(128,64) x 26 (91.990)</td><td>(256,128,64) x 26(94.890)</td></tr><tr><td>128 x 26(87.710)</td><td>(256,64) x 26 (92.370)</td><td>(512,256,128) x 26(95.00)</td></tr><tr><td>256 x 26(87.980)</td><td>(512,64) x 26 (93.120)</td><td><b>(1024,512,256) x 26(95.730)</b></td></tr><tr><td>512 x 26(87.230)</td><td><b>(1024,64) x 26(93.980)</b></td><td></td></tr><tr><td><b>1024 x 26(89.00)</b></td><td></td><td></td></tr></tbody></table>

## M₁ Model

Data in Tables 4 and 5 demonstrate how implementing fully linked layers in Resnet 50 improves performance. The output findings show that utilizing three fully connected layers provides the maximum accuracy compared to using two or one fully connected layers. Accuracy of 96.78% and 95.73% were achieved using a network consisting of 1024 neurons in the first layer, 512 neurons in the second layer, and 256 neurons in the activation layer. The ensemble takes this network topology as one of its inputs.

### 5.3.2 Densenet121

#### M₂ Model

In M₂ Model default input size is 224x224 but for our convenient we resize the input dimension as 180,180,3. The same parameters are used and the tabulation is shown below.

From Tables 6 and 7, it is noted that two fully connected layers work best for Densenet 121 rather than one fully connected layer. The results obtained for every procedure and tabulated. The first layer with 512 and the second layer with 64 showed the results of 96.89% and we fix this as in our architecture of densenet 121. This will be another input of the average ensemble architecture.
<table><caption>**Table 6.** Epoch = 50 Accuracy is tabulated below for the type of deeper dataset of set 1 by increasing the number of neurons</caption><thead><tr><th>One Fully Connected</th><th>Two Fully Connected</th></tr></thead><tbody><tr><td>32 × 13(85.660)</td><td>(64,32) × 13(94.760)</td></tr><tr><td>64 × 13(85.720)</td><td>(128,64) × 13(94.720)</td></tr><tr><td>128 × 13(85.450)</td><td>(256,64) × 13(95.20)</td></tr><tr><td>256 × 13(86.920)</td><td><b>(512,64) × 13(96.890)</b></td></tr><tr><td>512 × 13(87.980)</td><td>(1024,64) × 13(96.120)</td></tr><tr><td><b>1024 × 13(89.980)</b></td><td></td></tr></tbody></table>

<table><caption>**Table 7.** Epoch = 50 Accuracy is tabulated below for the dataset of set 3 by increasing the number of neurons.</caption><thead><tr><th>One Fully Connected</th><th>Two Fully Connected</th></tr></thead><tbody><tr><td>32 × 27(89.980)</td><td>(64,32) × 27 (96.880)</td></tr><tr><td>64 × 27 (90.340)</td><td>(128,64) × 27 (96.720)</td></tr><tr><td>128 × 27(91.340)</td><td>(256,64) × 27(97.460)</td></tr><tr><td>256 × 27(91.930)</td><td>(512,64) × 27(96.990)</td></tr><tr><td>512 × 27(93.140)</td><td><b>(1024,64) × 27(97.880)</b></td></tr><tr><td><b>1024 × 27(93.340)</b></td><td></td></tr></tbody></table>

<table><caption>**Table 8.** Comparison of the proposed with the existing results.</caption><thead><tr><th>Method</th><th>Deeper</th><th>Wider</th><th>Combined</th><th>Both</th></tr></thead><tbody><tr><td>Proposed Method</td><td>95.10</td><td>96.23</td><td><b>97.2</b></td><td><b>96.80</b></td></tr><tr><td>DenseNet</td><td>96.89</td><td>94.87</td><td>96.89</td><td>97.88</td></tr><tr><td>ResNet</td><td>92.55</td><td>96.78</td><td>96.78</td><td>95.73</td></tr></tbody></table>

The effectiveness of the suggested strategy with the existing methodology is shown in Table 8. Our proposed method has a maximum accuracy of 97.2%. The existing method seems quite time-consuming. Therefore, the suggested method is applicable to a wide variety of underwater images for precise classification.

# 6 Conclusion

We show that deep learning methods may be used to the task of classifying images of coral reefs in this research. The goal of this research is to standardize the process used to map the distribution of coral reef species and substrates. In this article, we
provide a comprehensive overview of the two most popular classes of existing deep
learning-based classification models and compare their features. Using point-by-point
ground-truth annotations, we first developed a fine-tuned CNN model and compared it to
state-of-the-art CNN-based architectures for image classification. Our best performing
recommended model for this task attained an accuracy of 97.2%. Prior efforts on this
subject, Dense Net and Resnet, attained accuracies of 96.7 and 96.8 percent, respectively.
The gathered data shows that, in contrast to the state-of-the-art systems presently in use,
the ensemble-tuned suggested CNN model outperforms them.

In the not-too-distant future, we will be able to create semantic maps of the whole
ecology of coral reefs. When all of the images from a particular location have been
examined and registered, the next step is to construct a two-dimensional semantic map
of the coral reef areas using the results of the registrations utilizing the photos. After that,
the mosaic ked image may then be reconstructed using superpixels. The suggested CNN
architecture allows for each super pixel to be subdivided into smaller patches, which
are subsequently categorized by the system. In order to classify face meshes, we use the
CNN algorithms that have been presented. All of the existing models discussed in this
article have been constructed on top of Dense net and Resnet, which act as the basis upon
which further improvements and alterations may be made. It may be possible to expand
this study in the future by altering these discoveries so that they can function with other
network topologies. Last but not least, considering that stereo cameras were used in the
process of image collection, there is scope for further investigation into the possibility
of include disparity information as a channel in the input picture. The utilization of
several perspectives to improve classification is a potential additional benefit that might
be achieved via the development of deep learning systems.

References

1. Elawady, M. Sparse coral classification using deep convolutional neural network. arXiv preprint arXiv:1511.09067 (2015)

2. Ferrario, F., Beck, M.W., Storlazzi, C.D., Micheli, F., Shepard, C.C., Airoldi, L.: The effec-
tiveness of coral reefs for coastal hazard risk reduction and adaptation. Nat. Commun. **5**, 3794
(2014). https://doi.org/10.1038/ncomms4794

3. Gomez-Villa, A., Salazar, A., Vargas, F.: Towards automatic wild animal monitoring: identifi-
cation of animal species in camera-trap images using very deep convolutional neural networks.
Eco. Inform. **41** (September), 24–32 (2017)

4. Mahmood, A., et al.: Automatic annotation of coral reefs using deep learning. In: OCEANS 2016 MTS/IEEE Monterey, pp. 1–5 (2016a)

5. Mahmood, A., et al.: Coral reef classification with hybrid feature representations. In: Image Processing (ICIP), 2016 IEEE International Conference on, pp. 519–523. IEEE (2016b). doi: https://doi.org/10.1109/ICIP.2016.7532411

6. Beijbom, O., Edmunds, P. J., Kline, D. I., Mitchell, B. G., Kriegman, D.: Automated annotation of coral reef survey images. In IEEE Conference on Computer Vision and Pattern Recognition, pp. 1170–77 (2012)

7. Shihavuddin, A.S.M., Gracias, N., Garcia, R., Gleason, A.C., Gintert, B.: Image-based coral reef classification and thematic mapping. Remote Sensing **5**(4), 1809–1841 (2013)

8. Stough, J.: Texture and color distribution-based classification for live coral detection. In: Proceedings of the 12th International Coral Reef Symposium, pp. 9–13 (2012)
9. Mary, N.A.B., Dharma, D.: Coral reef image classification employing improved LDP for feature extraction. J. Vis. Commun. Image Represent. **49**, 225–242 (2017). https://doi.org/10.1016/j.jvcir.2017.09.008

10. Mary, N.A.B., Dharma, D.: Classification of coral reef submarine images and videos using a novel Z with tilted Z local binary pattern (Z⊕TZLBP). Wireless Pers. Commun. **98**(3), 2427–2459 (2018). https://doi.org/10.1007/s11277-017-4981-x

11. Mary, N.A.B., Dharma, D.: Coral reef image/video classification employing novel octa-angled pattern for triangular sub region and pulse coupled convolutional neural network (PCCNN). Multimedia Tools Appl. **77**(24), 31545–31579 (2018). https://doi.org/10.1007/s11042-018-6148-5

12. Zhang, K., He, X., Ren, S., Sunm, J.: Deep residual learning for image recognition. In: IEEE CVPR (2016)

13. Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE CVPR, pp. 4700–4708 (2017)

14. Shabbeer Basha, S.H., Dubey, S.R., Pulabaigari, V., Mukherjee, S.: Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing **378**, 112–119 (2020). https://doi.org/10.1016/j.neucom.2019.10.008

15. Ganaie, M.A., Hu, M., Malik, A.K., Tanveer, M., Suganthan, P.N.: Ensemble deep learning: A review. Eng. Appl. Artif. Intell. **115**, 105151 (2022). https://doi.org/10.1016/j.engappai.2022.10515