Comparison of DenseNet-121 and MobileNet for Coral Reef Classification
Keywords:
Coral Reefs, Convolutional Neural Network, Confusion Matrix, DenseNet-121, MobileNet
Abstract
Coral reefs are a type of marine organism that has beauty and benefits for other sea creatures’ ecosystems. However, despite its beauty and usefulness, coral reefs are vulnerable to damage such as coral bleaching, which can impact other coral reef ecosystems. This research aims to classify digital images of healthy, bleached, and dead coral reefs. This research method is DenseNet-121 and MobileNet is based on Convolutional Neural Networks. This research uses a dataset from 1582 coral reef image data with three main classes: 720 were bleached, 150 were dead, and 712 were healthy. The testing process is carried out using several forms of split datasets, namely 60:10:30, 50:10:40, and 70:10:20. The test results obtained with a data sharing percentage of 60:10:30 show that MobileNet architecture achieved 88.00% accuracy, and DenseNet-121 achieved 91.57% accuracy. Using a data split percentage of 50:10:40, MobileNet achieved 84.51% accuracy, and DenseNet- 121 achieved 90.52% accuracy. Meanwhile, with a data separation percentage of 70:10:20, MobileNet achieved 85.48% accuracy, and DenseNet-121 achieved 92.74% accuracy.
Downloads
Download data is not yet available.
References
[1] E. Bayraktarov, A. T. Banaszak, P. Montoya Maya, J. Kleypas, J. E. Arias-Gonzlez, M. Blanco, J. Calle-Trivio, N. Charuvi,
C. Corts-Useche, V. Galvn, M. A. Garca Salgado, M. Gnecco, S. D. Guendulain-Garca, E. A. Hernndez Delgado, J. A.
Marn Moraga, M. F. Maya, S. Mendoza Quiroz, S. Mercado Cervantes, M. Morikawa, G. Nava, V. Pizarro, R. I.
Sellares-Blasco, S. E. Suleimn Ramos, T. Villalobos Cubero, M. F. Villalpando, and S. Fras-Torres, “Coral reef restoration
efforts in Latin American countries and territories,” PLOS ONE, vol. 15, no. 8, p. e0228477, Aug. 2020. [Online]. Available:
https://dx.plos.org/10.1371/journal.pone.0228477
[2] T. P. Hughes, J. T. Kerry, A. H. Baird, S. R. Connolly, T. J. Chase, A. Dietzel, T. Hill, A. S. Hoey, M. O. Hoogenboom,
M. Jacobson, A. Kerswell, J. S. Madin, A. Mieog, A. S. Paley, M. S. Pratchett, G. Torda, and R. M. Woods,
“Global warming impairs stockrecruitment dynamics of corals,” Nature, vol. 568, no. 7752, pp. 387–390, Apr. 2019,
https://doi.org/10.1038/s41586-019-1081-y. [Online]. Available: https://www.nature.com/articles/s41586-019-1081-y
[3] L. B. DeFilippo, L. C. McManus, D. E. Schindler, M. L. Pinsky, M. A. Colton, H. E. Fox, E. W. Tekwa, S. R.
Palumbi, T. E. Essington, and M. M. Webster, “Assessing the potential for demographic restoration and assisted evolution
to build climate resilience in coral reefs,” Ecological Applications, vol. 32, no. 7, p. e2650, Oct. 2022. [Online]. Available:
https://esajournals.onlinelibrary.wiley.com/doi/10.1002/eap.2650
[4] M. Slattery, M. S. Pankey, and M. P. Lesser, “Annual Thermal Stress Increases a Soft Corals Susceptibility to Bleaching,”
Scientific Reports, vol. 9, no. 1, p. 8064, May 2019, https://doi.org/10.1038/s41598-019-44566-9. [Online]. Available:
https://www.nature.com/articles/s41598-019-44566-9
[5] T. A. Courtney, B. B. Barnes, I. Chollett, R. Elahi, K. Gross, J. R. Guest, I. B. Kuffner, E. A. Lenz, H. R. Nelson,
C. S. Rogers, L. T. Toth, and A. J. Andersson, “Disturbances drive changes in coral community assemblages and coral
calcification capacity,” Ecosphere, vol. 11, no. 4, p. e03066, Apr. 2020, https://doi.org/10.1002/ecs2.3066. [Online]. Available:
https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.3066
[6] A. Raphael, Z. Dubinsky, D. Iluz, and N. S. Netanyahu, “Neural Network Recognition of Marine Benthos
and Corals,” Diversity, vol. 12, no. 1, p. 29, Jan. 2020, https://doi.org/10.3390/d12010029. [Online]. Available:
https://www.mdpi.com/1424-2818/12/1/29
[7] M. Asha Paul, P. Arockia Jansi Rani, and J. Liba Manopriya, “Gradient Based Aura Feature Extraction for
Coral Reef Classification,” Wireless Personal Communications, vol. 114, no. 1, pp. 149–166, Sep. 2020, https:
//doi.org/10.1007/s11277-020-07355-6. [Online]. Available: https://link.springer.com/10.1007/s11277-020-07355-6
[8] L. Von Chamier, R. F. Laine, J. Jukkala, C. Spahn, D. Krentzel, E. Nehme, M. Lerche, S. Hernndez-Prez, P. K. Mattila,
E. Karinou, S. Holden, A. C. Solak, A. Krull, T.-O. Buchholz, M. L. Jones, L. A. Royer, C. Leterrier, Y. Shechtman, F. Jug,
M. Heilemann, G. Jacquemet, and R. Henriques, “Democratising deep learning for microscopy with ZeroCostDL4Mic,”
Nature Communications, vol. 12, no. 1, p. 2276, Apr. 2021, https://doi.org/10.1038/s41467-021-22518-0. [Online]. Available:
https://www.nature.com/articles/s41467-021-22518-0
[9] P. Wang, E. Fan, and P. Wang, “Comparative analysis of image classification algorithms based on traditional machine learning
and deep learning,” Pattern Recognition Letters, vol. 141, pp. 61–67, Jan. 2021, https://doi.org/10.1016/j.patrec.2020.07.042.
[Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0167865520302981
[10] Agus Perdana Windarto, Anjar Wanto, S Solikhun, and Ronal Watrianthos, “A Comprehensive Bibliometric Analysis of Deep
Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023),” Jurnal RESTI (Rekayasa
Sistem dan Teknologi Informasi), vol. 7, no. 5, pp. 1155–1164, Oct. 2023, https://doi.org/10.29207/resti.v7i5.5274. [Online].
Available: http://jurnal.iaii.or.id/index.php/RESTI/article/view/5274
[11] J. Hu, X. Deng, Y. Pan, Y. Wang, and W. Jin, “Temporal Encoded Deep Learning Radiomics Model for
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma,” Journal of Medical and Biological
Engineering, vol. 43, no. 5, pp. 623–632, Oct. 2023, https://doi.org/10.1007/s40846-023-00829-5. [Online]. Available:
https://link.springer.com/10.1007/s40846-023-00829-5
[12] K. H. Manguri, R. N. Ramadhan, and P. R. Mohammed Amin, “Twitter Sentiment Analysis on Worldwide COVID-19
Outbreaks,” Kurdistan Journal of Applied Research, pp. 54–65, May 2020, https://doi.org/10.24017/covid.8. [Online].
Available: https://www.spu.edu.iq/kjar/index.php/kjar/article/view/512
[13] P. H. Kim, H. M. Yoon, J. R. Kim, J.-Y. Hwang, J.-H. Choi, J. Hwang, J. Lee, J. Sung, K.-H. Jung, B. Bae, A. Y. Jung,
Y. A. Cho, W. H. Shim, B. Bak, and J. S. Lee, “Bone Age Assessment Using Artificial Intelligence in Korean Pediatric
Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels,”
Korean Journal of Radiology, vol. 24, no. 11, p. 1151, 2023, https://doi.org/10.3348/kjr.2023.0092. [Online]. Available:
https://kjronline.org/DOIx.php?id=10.3348/kjr.2023.0092
[14] A. Esteva, K. Chou, S. Yeung, N. Naik, A. Madani, A. Mottaghi, Y. Liu, E. Topol, J. Dean, and R. Socher,
“Deep learning-enabled medical computer vision,” npj Digital Medicine, vol. 4, no. 1, p. 5, Jan. 2021, https:
//doi.org/10.1038/s41746-020-00376-2. [Online]. Available: https://www.nature.com/articles/s41746-020-00376-2
[15] D. R. Sarvamangala and R. V. Kulkarni, “Convolutional neural networks in medical image understanding: a survey,”
Evolutionary Intelligence, vol. 15, no. 1, pp. 1–22, Mar. 2022, https://doi.org/10.1007/s12065-020-00540-3. [Online].
Available: https://link.springer.com/10.1007/s12065-020-00540-3
[16] M. B. Bora, D. Daimary, K. Amitab, and D. Kandar, “Handwritten Character Recognition from Images using CNN-ECOC,”
Procedia Computer Science, vol. 167, pp. 2403–2409, 2020, https://doi.org/10.1016/j.procs.2020.03.293. [Online]. Available:
https://linkinghub.elsevier.com/retrieve/pii/S1877050920307596
[17] A. inar and M. Yildirim, “Detection of tumors on brain MRI images using the hybrid convolutional neural network
architecture,” Medical Hypotheses, vol. 139, p. 109684, Jun. 2020, https://doi.org/10.1016/j.mehy.2020.109684. [Online].
Available: https://linkinghub.elsevier.com/retrieve/pii/S0306987720301717
[18] J. Chen, D. Zhang, M. Suzauddola, Y. A. Nanehkaran, and Y. Sun, “Identification of plant disease images via a
squeezeandexcitation MobileNet model and twice transfer learning,” IET Image Processing, vol. 15, no. 5, pp. 1115–1127,
Apr. 2021, https://doi.org/10.1049/ipr2.12090. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1049/ipr2.12090
[19] Y.-D. Zhang, S. C. Satapathy, X. Zhang, and S.-H. Wang, “COVID-19 Diagnosis via DenseNet and Optimization of Transfer
Learning Setting,” Cognitive Computation, Jan. 2021, https://doi.org/10.1007/s12559-020-09776-8. [Online]. Available:
http://link.springer.com/10.1007/s12559-020-09776-8
[20] S. Das, O. F. M. R. R. Aranya, and N. N. Labiba, “Brain Tumor Classification Using Convolutional Neural Network,”
in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT).
Dhaka, Bangladesh: IEEE, May 2019, pp. 1–5, https://doi.org/10.1109/ICASERT.2019.8934603. [Online]. Available:
https://ieeexplore.ieee.org/document/8934603/
[21] A. A. Barbhuiya, R. K. Karsh, and R. Jain, “CNN based feature extraction and classification for sign language,” Multimedia
Tools and Applications, vol. 80, no. 2, pp. 3051–3069, Jan. 2021, https://doi.org/10.1007/s11042-020-09829-y. [Online].
Available: https://link.springer.com/10.1007/s11042-020-09829-y
[22] T. N. T. Arsad, E. A. Awalludin, Z. Bachok,W. N. J. H.W. Yussof, and M. S. Hitam, “A review of coral reef classification study
using deep learning approach,” Kuala Terengganu, Malaysia, 2023, p. 050005, https://doi.org/10.1063/5.0110245. [Online].
Available: https://pubs.aip.org/aip/acp/article/2879531
[23] D. Schrholz and A. Chennu, “Digitizing the coral reef: Machine learning of underwater spectral images enables
dense taxonomic mapping of benthic habitats,” Methods in Ecology and Evolution, vol. 14, no. 2, pp. 596–613, Feb.
2023, https://doi.org/10.1111/2041-210X.14029. [Online]. Available: https://besjournals.onlinelibrary.wiley.com/doi/10.1111/
2041-210X.14029
[24] M. D. M. Manessa, M. A. F. Ummam, A. F. Efriana, J. M. Semedi, and F. Ayu, “Assessing Derawan Islands Coral
Reefs over Two Decades: A Machine Learning Classification Perspective,” Sensors, vol. 24, no. 2, p. 466, Jan. 2024,
https://doi.org/10.3390/s24020466. [Online]. Available: https://www.mdpi.com/1424-8220/24/2/466
[25] S. Sakib, N. Ahmed, A. J. Kabir, and H. Ahmed, “An Overview of Convolutional Neural Network: Its Architecture and
Applications,” MATHEMATICS & COMPUTER SCIENCE, preprint, Nov. 2018, https://doi.org/10.20944/preprints201811.
0546.v1. [Online]. Available: http://www.preprints.org/manuscript/201811.0546/v1
[26] H. Gholamalinezhad and H. Khosravi, “Pooling Methods in Deep Neural Networks, a Review,” 2020, https:
//doi.org/10.48550/ARXIV.2009.07485. [Online]. Available: https://arxiv.org/abs/2009.07485
[27] X. Y. Wu, “A hand gesture recognition algorithm based on DC-CNN,” Multimedia Tools and Applications,
vol. 79, no. 13-14, pp. 9193–9205, Apr. 2020, https://doi.org/10.1007/s11042-019-7193-4. [Online]. Available:
http://link.springer.com/10.1007/s11042-019-7193-4
[28] S. M. Hassan, A. K. Maji, M. Jasiski, Z. Leonowicz, and E. Jasiska, “Identification of Plant-Leaf Diseases Using CNN and
Transfer-Learning Approach,” Electronics, vol. 10, no. 12, p. 1388, Jun. 2021, https://doi.org/10.3390/electronics10121388.
[Online]. Available: https://www.mdpi.com/2079-9292/10/12/1388
[29] K. Thenmozhi and U. Srinivasulu Reddy, “Crop pest classification based on deep convolutional neural network and transfer
learning,” Computers and Electronics in Agriculture, vol. 164, p. 104906, Sep. 2019, https://doi.org/10.1016/j.compag.2019.
104906. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0168169919310695
[30] T. Rahman, M. E. H. Chowdhury, A. Khandakar, K. R. Islam, K. F. Islam, Z. B. Mahbub, M. A. Kadir, and
S. Kashem, “Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest
X-ray,” Applied Sciences, vol. 10, no. 9, p. 3233, May 2020, https://doi.org/10.3390/app10093233. [Online]. Available:
https://www.mdpi.com/2076-3417/10/9/3233
[31] N. Aneja and S. Aneja, “Transfer Learning using CNN for Handwritten Devanagari Character Recognition,” in 2019 1st
International Conference on Advances in Information Technology (ICAIT). Chikmagalur, India: IEEE, Jul. 2019, pp. 293–296,
https://doi.org/10.1109/ICAIT47043.2019.8987286. [Online]. Available: https://ieeexplore.ieee.org/document/8987286/
[32] A. Susanto, C. A. Sari, E. H. Rachmawanto, I. U. W. Mulyono, and N. M. Yaacob, “A Comparative Study
of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19,” Scientific Journal
of Informatics, vol. 11, no. 1, pp. 31–40, Jan. 2024, https://doi.org/10.15294/sji.v11i1.47305. [Online]. Available:
https://journal.unnes.ac.id/nju/sji/article/view/47305
[33] K. M. Hosny, M. A. Kassem, and M. M. Foaud, “Classification of skin lesions using transfer learning and augmentation
with Alex-net,” PLOS ONE, vol. 14, no. 5, p. e0217293, May 2019, https://doi.org/10.1371/journal.pone.0217293. [Online].
Available: https://dx.plos.org/10.1371/journal.pone.0217293
[34] P. Chlap, H. Min, N. Vandenberg, J. Dowling, L. Holloway, and A. Haworth, “A review of medical image
data augmentation techniques for deep learning applications,” Journal of Medical Imaging and Radiation Oncology,
vol. 65, no. 5, pp. 545–563, Aug. 2021, https://doi.org/10.1111/1754-9485.13261. [Online]. Available: https:
//onlinelibrary.wiley.com/doi/10.1111/1754-9485.13261
C. Corts-Useche, V. Galvn, M. A. Garca Salgado, M. Gnecco, S. D. Guendulain-Garca, E. A. Hernndez Delgado, J. A.
Marn Moraga, M. F. Maya, S. Mendoza Quiroz, S. Mercado Cervantes, M. Morikawa, G. Nava, V. Pizarro, R. I.
Sellares-Blasco, S. E. Suleimn Ramos, T. Villalobos Cubero, M. F. Villalpando, and S. Fras-Torres, “Coral reef restoration
efforts in Latin American countries and territories,” PLOS ONE, vol. 15, no. 8, p. e0228477, Aug. 2020. [Online]. Available:
https://dx.plos.org/10.1371/journal.pone.0228477
[2] T. P. Hughes, J. T. Kerry, A. H. Baird, S. R. Connolly, T. J. Chase, A. Dietzel, T. Hill, A. S. Hoey, M. O. Hoogenboom,
M. Jacobson, A. Kerswell, J. S. Madin, A. Mieog, A. S. Paley, M. S. Pratchett, G. Torda, and R. M. Woods,
“Global warming impairs stockrecruitment dynamics of corals,” Nature, vol. 568, no. 7752, pp. 387–390, Apr. 2019,
https://doi.org/10.1038/s41586-019-1081-y. [Online]. Available: https://www.nature.com/articles/s41586-019-1081-y
[3] L. B. DeFilippo, L. C. McManus, D. E. Schindler, M. L. Pinsky, M. A. Colton, H. E. Fox, E. W. Tekwa, S. R.
Palumbi, T. E. Essington, and M. M. Webster, “Assessing the potential for demographic restoration and assisted evolution
to build climate resilience in coral reefs,” Ecological Applications, vol. 32, no. 7, p. e2650, Oct. 2022. [Online]. Available:
https://esajournals.onlinelibrary.wiley.com/doi/10.1002/eap.2650
[4] M. Slattery, M. S. Pankey, and M. P. Lesser, “Annual Thermal Stress Increases a Soft Corals Susceptibility to Bleaching,”
Scientific Reports, vol. 9, no. 1, p. 8064, May 2019, https://doi.org/10.1038/s41598-019-44566-9. [Online]. Available:
https://www.nature.com/articles/s41598-019-44566-9
[5] T. A. Courtney, B. B. Barnes, I. Chollett, R. Elahi, K. Gross, J. R. Guest, I. B. Kuffner, E. A. Lenz, H. R. Nelson,
C. S. Rogers, L. T. Toth, and A. J. Andersson, “Disturbances drive changes in coral community assemblages and coral
calcification capacity,” Ecosphere, vol. 11, no. 4, p. e03066, Apr. 2020, https://doi.org/10.1002/ecs2.3066. [Online]. Available:
https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecs2.3066
[6] A. Raphael, Z. Dubinsky, D. Iluz, and N. S. Netanyahu, “Neural Network Recognition of Marine Benthos
and Corals,” Diversity, vol. 12, no. 1, p. 29, Jan. 2020, https://doi.org/10.3390/d12010029. [Online]. Available:
https://www.mdpi.com/1424-2818/12/1/29
[7] M. Asha Paul, P. Arockia Jansi Rani, and J. Liba Manopriya, “Gradient Based Aura Feature Extraction for
Coral Reef Classification,” Wireless Personal Communications, vol. 114, no. 1, pp. 149–166, Sep. 2020, https:
//doi.org/10.1007/s11277-020-07355-6. [Online]. Available: https://link.springer.com/10.1007/s11277-020-07355-6
[8] L. Von Chamier, R. F. Laine, J. Jukkala, C. Spahn, D. Krentzel, E. Nehme, M. Lerche, S. Hernndez-Prez, P. K. Mattila,
E. Karinou, S. Holden, A. C. Solak, A. Krull, T.-O. Buchholz, M. L. Jones, L. A. Royer, C. Leterrier, Y. Shechtman, F. Jug,
M. Heilemann, G. Jacquemet, and R. Henriques, “Democratising deep learning for microscopy with ZeroCostDL4Mic,”
Nature Communications, vol. 12, no. 1, p. 2276, Apr. 2021, https://doi.org/10.1038/s41467-021-22518-0. [Online]. Available:
https://www.nature.com/articles/s41467-021-22518-0
[9] P. Wang, E. Fan, and P. Wang, “Comparative analysis of image classification algorithms based on traditional machine learning
and deep learning,” Pattern Recognition Letters, vol. 141, pp. 61–67, Jan. 2021, https://doi.org/10.1016/j.patrec.2020.07.042.
[Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0167865520302981
[10] Agus Perdana Windarto, Anjar Wanto, S Solikhun, and Ronal Watrianthos, “A Comprehensive Bibliometric Analysis of Deep
Learning Techniques for Breast Cancer Segmentation: Trends and Topic Exploration (2019-2023),” Jurnal RESTI (Rekayasa
Sistem dan Teknologi Informasi), vol. 7, no. 5, pp. 1155–1164, Oct. 2023, https://doi.org/10.29207/resti.v7i5.5274. [Online].
Available: http://jurnal.iaii.or.id/index.php/RESTI/article/view/5274
[11] J. Hu, X. Deng, Y. Pan, Y. Wang, and W. Jin, “Temporal Encoded Deep Learning Radiomics Model for
Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma,” Journal of Medical and Biological
Engineering, vol. 43, no. 5, pp. 623–632, Oct. 2023, https://doi.org/10.1007/s40846-023-00829-5. [Online]. Available:
https://link.springer.com/10.1007/s40846-023-00829-5
[12] K. H. Manguri, R. N. Ramadhan, and P. R. Mohammed Amin, “Twitter Sentiment Analysis on Worldwide COVID-19
Outbreaks,” Kurdistan Journal of Applied Research, pp. 54–65, May 2020, https://doi.org/10.24017/covid.8. [Online].
Available: https://www.spu.edu.iq/kjar/index.php/kjar/article/view/512
[13] P. H. Kim, H. M. Yoon, J. R. Kim, J.-Y. Hwang, J.-H. Choi, J. Hwang, J. Lee, J. Sung, K.-H. Jung, B. Bae, A. Y. Jung,
Y. A. Cho, W. H. Shim, B. Bak, and J. S. Lee, “Bone Age Assessment Using Artificial Intelligence in Korean Pediatric
Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels,”
Korean Journal of Radiology, vol. 24, no. 11, p. 1151, 2023, https://doi.org/10.3348/kjr.2023.0092. [Online]. Available:
https://kjronline.org/DOIx.php?id=10.3348/kjr.2023.0092
[14] A. Esteva, K. Chou, S. Yeung, N. Naik, A. Madani, A. Mottaghi, Y. Liu, E. Topol, J. Dean, and R. Socher,
“Deep learning-enabled medical computer vision,” npj Digital Medicine, vol. 4, no. 1, p. 5, Jan. 2021, https:
//doi.org/10.1038/s41746-020-00376-2. [Online]. Available: https://www.nature.com/articles/s41746-020-00376-2
[15] D. R. Sarvamangala and R. V. Kulkarni, “Convolutional neural networks in medical image understanding: a survey,”
Evolutionary Intelligence, vol. 15, no. 1, pp. 1–22, Mar. 2022, https://doi.org/10.1007/s12065-020-00540-3. [Online].
Available: https://link.springer.com/10.1007/s12065-020-00540-3
[16] M. B. Bora, D. Daimary, K. Amitab, and D. Kandar, “Handwritten Character Recognition from Images using CNN-ECOC,”
Procedia Computer Science, vol. 167, pp. 2403–2409, 2020, https://doi.org/10.1016/j.procs.2020.03.293. [Online]. Available:
https://linkinghub.elsevier.com/retrieve/pii/S1877050920307596
[17] A. inar and M. Yildirim, “Detection of tumors on brain MRI images using the hybrid convolutional neural network
architecture,” Medical Hypotheses, vol. 139, p. 109684, Jun. 2020, https://doi.org/10.1016/j.mehy.2020.109684. [Online].
Available: https://linkinghub.elsevier.com/retrieve/pii/S0306987720301717
[18] J. Chen, D. Zhang, M. Suzauddola, Y. A. Nanehkaran, and Y. Sun, “Identification of plant disease images via a
squeezeandexcitation MobileNet model and twice transfer learning,” IET Image Processing, vol. 15, no. 5, pp. 1115–1127,
Apr. 2021, https://doi.org/10.1049/ipr2.12090. [Online]. Available: https://onlinelibrary.wiley.com/doi/10.1049/ipr2.12090
[19] Y.-D. Zhang, S. C. Satapathy, X. Zhang, and S.-H. Wang, “COVID-19 Diagnosis via DenseNet and Optimization of Transfer
Learning Setting,” Cognitive Computation, Jan. 2021, https://doi.org/10.1007/s12559-020-09776-8. [Online]. Available:
http://link.springer.com/10.1007/s12559-020-09776-8
[20] S. Das, O. F. M. R. R. Aranya, and N. N. Labiba, “Brain Tumor Classification Using Convolutional Neural Network,”
in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT).
Dhaka, Bangladesh: IEEE, May 2019, pp. 1–5, https://doi.org/10.1109/ICASERT.2019.8934603. [Online]. Available:
https://ieeexplore.ieee.org/document/8934603/
[21] A. A. Barbhuiya, R. K. Karsh, and R. Jain, “CNN based feature extraction and classification for sign language,” Multimedia
Tools and Applications, vol. 80, no. 2, pp. 3051–3069, Jan. 2021, https://doi.org/10.1007/s11042-020-09829-y. [Online].
Available: https://link.springer.com/10.1007/s11042-020-09829-y
[22] T. N. T. Arsad, E. A. Awalludin, Z. Bachok,W. N. J. H.W. Yussof, and M. S. Hitam, “A review of coral reef classification study
using deep learning approach,” Kuala Terengganu, Malaysia, 2023, p. 050005, https://doi.org/10.1063/5.0110245. [Online].
Available: https://pubs.aip.org/aip/acp/article/2879531
[23] D. Schrholz and A. Chennu, “Digitizing the coral reef: Machine learning of underwater spectral images enables
dense taxonomic mapping of benthic habitats,” Methods in Ecology and Evolution, vol. 14, no. 2, pp. 596–613, Feb.
2023, https://doi.org/10.1111/2041-210X.14029. [Online]. Available: https://besjournals.onlinelibrary.wiley.com/doi/10.1111/
2041-210X.14029
[24] M. D. M. Manessa, M. A. F. Ummam, A. F. Efriana, J. M. Semedi, and F. Ayu, “Assessing Derawan Islands Coral
Reefs over Two Decades: A Machine Learning Classification Perspective,” Sensors, vol. 24, no. 2, p. 466, Jan. 2024,
https://doi.org/10.3390/s24020466. [Online]. Available: https://www.mdpi.com/1424-8220/24/2/466
[25] S. Sakib, N. Ahmed, A. J. Kabir, and H. Ahmed, “An Overview of Convolutional Neural Network: Its Architecture and
Applications,” MATHEMATICS & COMPUTER SCIENCE, preprint, Nov. 2018, https://doi.org/10.20944/preprints201811.
0546.v1. [Online]. Available: http://www.preprints.org/manuscript/201811.0546/v1
[26] H. Gholamalinezhad and H. Khosravi, “Pooling Methods in Deep Neural Networks, a Review,” 2020, https:
//doi.org/10.48550/ARXIV.2009.07485. [Online]. Available: https://arxiv.org/abs/2009.07485
[27] X. Y. Wu, “A hand gesture recognition algorithm based on DC-CNN,” Multimedia Tools and Applications,
vol. 79, no. 13-14, pp. 9193–9205, Apr. 2020, https://doi.org/10.1007/s11042-019-7193-4. [Online]. Available:
http://link.springer.com/10.1007/s11042-019-7193-4
[28] S. M. Hassan, A. K. Maji, M. Jasiski, Z. Leonowicz, and E. Jasiska, “Identification of Plant-Leaf Diseases Using CNN and
Transfer-Learning Approach,” Electronics, vol. 10, no. 12, p. 1388, Jun. 2021, https://doi.org/10.3390/electronics10121388.
[Online]. Available: https://www.mdpi.com/2079-9292/10/12/1388
[29] K. Thenmozhi and U. Srinivasulu Reddy, “Crop pest classification based on deep convolutional neural network and transfer
learning,” Computers and Electronics in Agriculture, vol. 164, p. 104906, Sep. 2019, https://doi.org/10.1016/j.compag.2019.
104906. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S0168169919310695
[30] T. Rahman, M. E. H. Chowdhury, A. Khandakar, K. R. Islam, K. F. Islam, Z. B. Mahbub, M. A. Kadir, and
S. Kashem, “Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection Using Chest
X-ray,” Applied Sciences, vol. 10, no. 9, p. 3233, May 2020, https://doi.org/10.3390/app10093233. [Online]. Available:
https://www.mdpi.com/2076-3417/10/9/3233
[31] N. Aneja and S. Aneja, “Transfer Learning using CNN for Handwritten Devanagari Character Recognition,” in 2019 1st
International Conference on Advances in Information Technology (ICAIT). Chikmagalur, India: IEEE, Jul. 2019, pp. 293–296,
https://doi.org/10.1109/ICAIT47043.2019.8987286. [Online]. Available: https://ieeexplore.ieee.org/document/8987286/
[32] A. Susanto, C. A. Sari, E. H. Rachmawanto, I. U. W. Mulyono, and N. M. Yaacob, “A Comparative Study
of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19,” Scientific Journal
of Informatics, vol. 11, no. 1, pp. 31–40, Jan. 2024, https://doi.org/10.15294/sji.v11i1.47305. [Online]. Available:
https://journal.unnes.ac.id/nju/sji/article/view/47305
[33] K. M. Hosny, M. A. Kassem, and M. M. Foaud, “Classification of skin lesions using transfer learning and augmentation
with Alex-net,” PLOS ONE, vol. 14, no. 5, p. e0217293, May 2019, https://doi.org/10.1371/journal.pone.0217293. [Online].
Available: https://dx.plos.org/10.1371/journal.pone.0217293
[34] P. Chlap, H. Min, N. Vandenberg, J. Dowling, L. Holloway, and A. Haworth, “A review of medical image
data augmentation techniques for deep learning applications,” Journal of Medical Imaging and Radiation Oncology,
vol. 65, no. 5, pp. 545–563, Aug. 2021, https://doi.org/10.1111/1754-9485.13261. [Online]. Available: https:
//onlinelibrary.wiley.com/doi/10.1111/1754-9485.13261
Published
2024-03-08
How to Cite
Hadi, H. P., Rachmawanto, E. H., & Ali, R. R. (2024). Comparison of DenseNet-121 and MobileNet for Coral Reef Classification. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 23(2), 333-342. https://doi.org/https://doi.org/10.30812/matrik.v23i2.3683
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
.png)
