Araştırma Makalesi
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Yıl 2023, Cilt: 72 Sayı: 2, 482 - 499, 23.06.2023

Öznur Özaltın Özgür Yeniay

https://doi.org/10.31801/cfsuasmas.1202806

Öz

Kaynakça

  • Ladnyj, I., Ziegler, P., Kima, E., A human infection caused by monkeypox virus in Basankusu Territory, Democratic Republic of the Congo, Bulletin of the World Health Organization, 46(5) (1972), 593.
  • Thornhill, J. P., Barkati, S., Walmsley, S., Rockstroh, J., Antinori, A., Harrison, L. B., Palich, R., Nori, A., Reeves, I., Habibi, M. S., Apea, V., Boesecke, C., Vandekerckhove, L., Yakubovsky, M., Sendagorta, E., Blanco, J. L., Florence, E., Moschese, D., Maltez, F. M., Goorhuis, A., Pourcher, V., Migaud, P., Noe, S., Pintado, C., Maggi, F., Hansen, A. E., Hoffmann, C., Lezama, J. I., Mussini, C., Cattelan, A., Makofane, K., Tan, D., Nozza, S., Nemeth, J., Klein, M. B., Orkin, C. M., SHARE-net Clinical Group, Monkeypox virus infection in humans across 16 countries-April–June 2022, New England Journal of Medicine, 387(8) (2022), 679-691. doi:10.1056/NEJMoa2207323
  • Aplogan, A., Szczeniowski, M., Human monkeypox–Kasai Oriental, Democratic Republic of Congo, MMWR: Morbidity & Mortality Weekly Report, 46(49) (1997), 1168-1171.
  • Durski, K. N., McCollum, A. M., Nakazawa, Y., Petersen B. W., Reynolds, M. G., Briand, S., Djingarey, M. H., Olson, V., Damon, I. K., Khalakdina, A., Emergence of monkeypoxwest and central Africa, 1970-2017, Morbidity and Mortality Weekly Report, 67(10) (2018), 306-310.doi:10.15585/mmwr.mm6710a5
  • Vaughan, A., Aarons, E., Astbury, J., Balasegaram, S., Beadsworth, M., Beck, C. R., Chand, M., O’Connor, C., Dunning, J., Ghebrehewet, S., Harper, N., Howlett-Shipley, R., Ihekweazu, C., Jacobs, M., Kaindama, L., Katwa, P., Khoo, S., Lamb, L., Mawdsley, S., Morgan, D., Palmer, R., Phin, N., Russell, K., Said, B., Simpson, A., Vivancos, R., Wade, M., Walsh, A., Wilburn, J., Two cases of monkeypox imported to the United Kingdom, September 2018, Eurosurveillance, 23(38) (2018), 1800509. https://doi.org/10.2807/1560-7917.ES.2018.23.38.1800509
  • Erez, N., Achdout, H., Milrot, E., Schwartz, Y., Wiener-Well, Y., Paran, N., Politi, B., Tamir, H., Israely, T., Weiss, S., Beth-Din, A., Shifman, O., Israeli, O., Yitzhaki, S., Shapira, S. C., Melamed, S., Schwartz, E., Diagnosis of imported monkeypox, Israel, 2018, Emerging Infectious Diseases, 25(5) (2019), 980-983. doi:10.3201/eid2505.190076
  • Bunge, E. M., Hoet, B., Chen, L., Lienert, F., Weidenthaler, H., Baer, L. R., Steffen, R., The changing epidemiology of human monkeypox-A potential threat? A systematic review, PLoS Neglected Tropical Diseases, 16(2) (2022), e0010141.https://doi.org/10.1371/journal.pntd.0010141
  • Organization WH. Multi-country monkeypox outbreak, situation update, (2022).
  • Özaltın, Ö., Köklü, M., Yonar, A., Yeniay, ¨ O., Automatically image classification based on a new CNN architecture, III International Applied Statistics Conference (UYIK - 2022), Skopje, N Macedonia, 22-24 June 2022, (2022), 21-32.
  • Ozaltin, O., Coskun, O., Yeniay, O., Subasi, A., Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm, International Journal of Imaging Systems and Technology, (2022), 1-23. https://doi.org/10.1002/ima.22806
  • Özaltın, Ö., Yeniay, Ö., Ecg classification performing feature extraction automatically using a hybrid cnn-svm algorithm, IEEE, 2021 3rd International Congress on Human- Computer Interaction, Optimization and Robotic Applications (HORA), (2021), 1-5. doi:10.1109/HORA52670.2021.9461295
  • Koklu, M., Unlersen, M. F., Ozkan, I. A., Aslan, M. F., Sabanci, K., A CNN-SVM study based on selected deep features for grapevine leaves classification, Measurement, 188 (2022), 110425. https://doi.org/10.1016/j.measurement.2021.110425
  • Tuncer, T., Ozyurt, F., Dogan, S., Subasi, A., A novel Covid-19 and pneumonia classification method based on F-transform, Chemometrics and Intelligent Laboratory Systems, 210 (2021), 104256.https://doi.org/10.1016/j.chemolab.2021.104256
  • Ozaltin, O., Coskun, O., Yeniay, O., Subasi, A., A deep learning approach for detecting stroke from brain CT images using OzNet, Bioengineering, 9(12) (2022), 783. https://doi.org/10.3390/bioengineering9120783
  • Ozaltin, O., Yeniay, O., A novel proposed CNN-SVM architecture for ECG scalograms classification, Soft Computing, (2022). https://doi.org/10.1007/s00500-022-07729-x
  • Sahin, V. H., Oztel, I., Yolcu Oztel, G., Human monkeypox classification from skin lesion images with deep pre-trained network using mobile application, Journal of Medical Systems, 46(11) (2022), 1-10. https://doi.org/10.1007/s10916-022-01863-7
  • Ali, S. N., Ahmed, M., Paul, J., Jahan, T., Sani, S., Noor, N., Hasan, T., Monkeypox skin lesion detection using deep learning models: A feasibility study, arXiv preprint arXiv:220703342,(2022). https://doi.org/10.48550/arXiv.2207.03342
  • Ahsan, M. M., Uddin, M. R., Farjana, M., Sakib, A. N., Momin, K. A., Luna, S. A., Image data collection and implementation of deep learning-based model in detecting monkeypox disease using modified VGG16, arXiv preprint arXiv:220601862, (2022). https://doi.org/10.48550/arXiv.2206.01862
  • Alakus, T. B., Baykara, M., Comparison of monkeypox and wart DNA sequences with deep learning model, Applied Sciences, 12(20) (2022), 10216. https://doi.org/10.3390/app122010216
  • Sitaula, C., Shahi, T. B., Monkeypox virus detection using pre-trained deep learning based approaches, Journal of Medical Systems, 46(11) (2022), 1-9. https://doi.org/10.1007/s10916-022-01868-2
  • Akin, K. D., Gurkan, C., Budak, A., Karatas, H., Classification of monkeypox skin lesion using the explainable artificial intelligence assisted convolutional neural networks, Avrupa Bilim ve Teknoloji Dergisi, 40 (2022), 106-10. https://doi.org/10.31590/ejosat.1171816
  • Abdelhamid, A. A., El-Kenawy, E-SM., Khodadadi, N., Mirjalili, S., Khafaga, D. S., Alharbi, A. H., Ibrahim, A., Eid, M. M., Saber, M., Classification of monkeypox images based on transfer learning and the Al-Biruni earth radius optimization algorithm, Mathematics, 10(19) (2022), 3614. https://doi.org/10.3390/math10193614
  • Yamashita, R., Nishio, M., Do, R. K. G., Togashi, K., Convolutional neural networks: an overview and application in radiology, Insights Into Imaging, 9(4) (2018), 611-29.
  • Albawi, S., Mohammed, T. A., Al-Zawi, S., Understanding of a convolutional neural network, 2017 International Conference on Engineering and Technology (ICET), IEEE, (2017). doi:10.1109/ICEngTechnol.2017.8308186
  • Hubel, D. H., Wiesel, T. N., Receptive fields and functional architecture of monkey striate cortex, The Journal of Physiology, 195(1) (1968), 215-43. https://doi.org/10.1113/jphysiol.1968.sp008455
  • Fukushima, K., Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, 36 (1980), 193-202.
  • Bilbrey, J. A., Heindel, J. P., Schram, M., Bandyopadhyay, P., Xantheas, S. S., Choudhury, S., A look inside the black box: Using graph-theoretical descriptors to interpret a continuous-filter convolutional neural network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters, The Journal of Chemical Physics, 153(2) (2020), 024302.https://doi.org/10.1063/5.0009933
  • Baloglu, U. B., Talo, M., Yildirim, O., San Tan, R., Acharya, U. R., Classification of myocardial infarction with multi-lead ECG signals and deep CNN, Pattern Recognition Letters, 122 (2019), 23-30. https://doi.org/10.1016/j.patrec.2019.02.016
  • Acharya, U. R., Fujita, H., Oh SL., Hagiwara, Y., Tan, J. H., Adam, M., Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals, Information Sciences, 415 (2017), 190-8. https://doi.org/10.1016/j.ins.2017.06.027
  • Lee, H. K., Choi, Y. S., Application of continuous wavelet transform and convolutional neural network in decoding motor imagery brain-computer interface, Entropy, 21(12) (2019), 1199. https://doi.org/10.3390/e21121199
  • Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., Mobilenets: Efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:170404861, (2017). https://doi.org/10.48550/arXiv.1704.04861
  • Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L. C., Mobilenetv2: Inverted residuals and linear bottlenecks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018).
  • Sutskever, I., Martens, J., Dahl, G., Hinton, G., On the importance of initialization and momentum in deep learning, International Conference on Machine Learning, (2013) PMLR. https://proceedings.mlr.press/v28/sutskever13.html
  • Yang, J., Yang, G., Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer, Algorithms, 11(3) (2018), 28. https://doi.org/10.3390/a11030028
  • Kingma, D. P., Ba, J., Adam: A method for stochastic optimization, arXiv preprint arXiv:14126980, (2014). https://doi.org/10.48550/arXiv.1412.6980
  • Tieleman, T., Hinton, G., Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, Coursera: Neural networks for machine learning, 4(2) (2012), 26-31.
  • McHugh, M. L., The chi-square test of independence, Biochemia Medica, 23(2) (2013), 143-9. https://doi.org/10.11613/BM.2013.018
  • Sharpe, D., Chi-square test is statistically significant: Now what?, Practical Assessment, Research, and Evaluation, 20(1) (2015), 8. https://doi.org/10.7275/tbfa-x148
  • Sankaran, M., Approximations to the non-central chi-square distribution, Biometrika, 50(1/2) (1963), 199-204.
  • Widodo, A., Yang, B-S., Support vector machine in machine condition monitoring and fault diagnosis, Mechanical Systems and Signal Processing, 21(6) (2007), 2560-74. https://doi.org/10.1016/j.ymssp.2006.12.007
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Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture

Yıl 2023, Cilt: 72 Sayı: 2, 482 - 499, 23.06.2023

Öznur Özaltın Özgür Yeniay

https://doi.org/10.31801/cfsuasmas.1202806

Öz

Monkeypox has recently become an endemic disease that threatens the whole world. The most distinctive feature of this disease is occurring skin lesions. However, in other types of diseases such as chickenpox, measles, and smallpox skin lesions can also be seen. The main aim of this study was to quickly detect monkeypox disease from others through deep learning approaches based on skin images. In this study, MobileNetv2 was used to determine in images whether it was monkeypox or non-monkeypox. To find splitting methods and optimization methods, a comprehensive analysis was performed. The splitting methods included training and testing (70:30 and 80:20) and 10 fold cross validation. The optimization methods as adaptive moment estimation (adam), root mean square propagation (rmsprop), and stochastic gradient descent momentum (sgdm) were used. Then, MobileNetv2 was tasked as a deep feature extractor and features were obtained from the global pooling layer. The Chi-Square feature selection method was used to reduce feature dimensions. Finally, selected features were classified using the Support Vector Machine (SVM) with different kernel functions. In this study, 10 fold cross validation and adam were seen as the best splitting and optimization methods, respectively, with an accuracy of 98.59%. Then, significant features were selected via the Chi-Square method and while classifying 500
features with SVM, an accuracy of 99.69% was observed.

Anahtar Kelimeler

Chi-Square method deep learning feature selection monkeypox optimization

Kaynakça

  • Ladnyj, I., Ziegler, P., Kima, E., A human infection caused by monkeypox virus in Basankusu Territory, Democratic Republic of the Congo, Bulletin of the World Health Organization, 46(5) (1972), 593.
  • Thornhill, J. P., Barkati, S., Walmsley, S., Rockstroh, J., Antinori, A., Harrison, L. B., Palich, R., Nori, A., Reeves, I., Habibi, M. S., Apea, V., Boesecke, C., Vandekerckhove, L., Yakubovsky, M., Sendagorta, E., Blanco, J. L., Florence, E., Moschese, D., Maltez, F. M., Goorhuis, A., Pourcher, V., Migaud, P., Noe, S., Pintado, C., Maggi, F., Hansen, A. E., Hoffmann, C., Lezama, J. I., Mussini, C., Cattelan, A., Makofane, K., Tan, D., Nozza, S., Nemeth, J., Klein, M. B., Orkin, C. M., SHARE-net Clinical Group, Monkeypox virus infection in humans across 16 countries-April–June 2022, New England Journal of Medicine, 387(8) (2022), 679-691. doi:10.1056/NEJMoa2207323
  • Aplogan, A., Szczeniowski, M., Human monkeypox–Kasai Oriental, Democratic Republic of Congo, MMWR: Morbidity & Mortality Weekly Report, 46(49) (1997), 1168-1171.
  • Durski, K. N., McCollum, A. M., Nakazawa, Y., Petersen B. W., Reynolds, M. G., Briand, S., Djingarey, M. H., Olson, V., Damon, I. K., Khalakdina, A., Emergence of monkeypoxwest and central Africa, 1970-2017, Morbidity and Mortality Weekly Report, 67(10) (2018), 306-310.doi:10.15585/mmwr.mm6710a5
  • Vaughan, A., Aarons, E., Astbury, J., Balasegaram, S., Beadsworth, M., Beck, C. R., Chand, M., O’Connor, C., Dunning, J., Ghebrehewet, S., Harper, N., Howlett-Shipley, R., Ihekweazu, C., Jacobs, M., Kaindama, L., Katwa, P., Khoo, S., Lamb, L., Mawdsley, S., Morgan, D., Palmer, R., Phin, N., Russell, K., Said, B., Simpson, A., Vivancos, R., Wade, M., Walsh, A., Wilburn, J., Two cases of monkeypox imported to the United Kingdom, September 2018, Eurosurveillance, 23(38) (2018), 1800509. https://doi.org/10.2807/1560-7917.ES.2018.23.38.1800509
  • Erez, N., Achdout, H., Milrot, E., Schwartz, Y., Wiener-Well, Y., Paran, N., Politi, B., Tamir, H., Israely, T., Weiss, S., Beth-Din, A., Shifman, O., Israeli, O., Yitzhaki, S., Shapira, S. C., Melamed, S., Schwartz, E., Diagnosis of imported monkeypox, Israel, 2018, Emerging Infectious Diseases, 25(5) (2019), 980-983. doi:10.3201/eid2505.190076
  • Bunge, E. M., Hoet, B., Chen, L., Lienert, F., Weidenthaler, H., Baer, L. R., Steffen, R., The changing epidemiology of human monkeypox-A potential threat? A systematic review, PLoS Neglected Tropical Diseases, 16(2) (2022), e0010141.https://doi.org/10.1371/journal.pntd.0010141
  • Organization WH. Multi-country monkeypox outbreak, situation update, (2022).
  • Özaltın, Ö., Köklü, M., Yonar, A., Yeniay, ¨ O., Automatically image classification based on a new CNN architecture, III International Applied Statistics Conference (UYIK - 2022), Skopje, N Macedonia, 22-24 June 2022, (2022), 21-32.
  • Ozaltin, O., Coskun, O., Yeniay, O., Subasi, A., Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm, International Journal of Imaging Systems and Technology, (2022), 1-23. https://doi.org/10.1002/ima.22806
  • Özaltın, Ö., Yeniay, Ö., Ecg classification performing feature extraction automatically using a hybrid cnn-svm algorithm, IEEE, 2021 3rd International Congress on Human- Computer Interaction, Optimization and Robotic Applications (HORA), (2021), 1-5. doi:10.1109/HORA52670.2021.9461295
  • Koklu, M., Unlersen, M. F., Ozkan, I. A., Aslan, M. F., Sabanci, K., A CNN-SVM study based on selected deep features for grapevine leaves classification, Measurement, 188 (2022), 110425. https://doi.org/10.1016/j.measurement.2021.110425
  • Tuncer, T., Ozyurt, F., Dogan, S., Subasi, A., A novel Covid-19 and pneumonia classification method based on F-transform, Chemometrics and Intelligent Laboratory Systems, 210 (2021), 104256.https://doi.org/10.1016/j.chemolab.2021.104256
  • Ozaltin, O., Coskun, O., Yeniay, O., Subasi, A., A deep learning approach for detecting stroke from brain CT images using OzNet, Bioengineering, 9(12) (2022), 783. https://doi.org/10.3390/bioengineering9120783
  • Ozaltin, O., Yeniay, O., A novel proposed CNN-SVM architecture for ECG scalograms classification, Soft Computing, (2022). https://doi.org/10.1007/s00500-022-07729-x
  • Sahin, V. H., Oztel, I., Yolcu Oztel, G., Human monkeypox classification from skin lesion images with deep pre-trained network using mobile application, Journal of Medical Systems, 46(11) (2022), 1-10. https://doi.org/10.1007/s10916-022-01863-7
  • Ali, S. N., Ahmed, M., Paul, J., Jahan, T., Sani, S., Noor, N., Hasan, T., Monkeypox skin lesion detection using deep learning models: A feasibility study, arXiv preprint arXiv:220703342,(2022). https://doi.org/10.48550/arXiv.2207.03342
  • Ahsan, M. M., Uddin, M. R., Farjana, M., Sakib, A. N., Momin, K. A., Luna, S. A., Image data collection and implementation of deep learning-based model in detecting monkeypox disease using modified VGG16, arXiv preprint arXiv:220601862, (2022). https://doi.org/10.48550/arXiv.2206.01862
  • Alakus, T. B., Baykara, M., Comparison of monkeypox and wart DNA sequences with deep learning model, Applied Sciences, 12(20) (2022), 10216. https://doi.org/10.3390/app122010216
  • Sitaula, C., Shahi, T. B., Monkeypox virus detection using pre-trained deep learning based approaches, Journal of Medical Systems, 46(11) (2022), 1-9. https://doi.org/10.1007/s10916-022-01868-2
  • Akin, K. D., Gurkan, C., Budak, A., Karatas, H., Classification of monkeypox skin lesion using the explainable artificial intelligence assisted convolutional neural networks, Avrupa Bilim ve Teknoloji Dergisi, 40 (2022), 106-10. https://doi.org/10.31590/ejosat.1171816
  • Abdelhamid, A. A., El-Kenawy, E-SM., Khodadadi, N., Mirjalili, S., Khafaga, D. S., Alharbi, A. H., Ibrahim, A., Eid, M. M., Saber, M., Classification of monkeypox images based on transfer learning and the Al-Biruni earth radius optimization algorithm, Mathematics, 10(19) (2022), 3614. https://doi.org/10.3390/math10193614
  • Yamashita, R., Nishio, M., Do, R. K. G., Togashi, K., Convolutional neural networks: an overview and application in radiology, Insights Into Imaging, 9(4) (2018), 611-29.
  • Albawi, S., Mohammed, T. A., Al-Zawi, S., Understanding of a convolutional neural network, 2017 International Conference on Engineering and Technology (ICET), IEEE, (2017). doi:10.1109/ICEngTechnol.2017.8308186
  • Hubel, D. H., Wiesel, T. N., Receptive fields and functional architecture of monkey striate cortex, The Journal of Physiology, 195(1) (1968), 215-43. https://doi.org/10.1113/jphysiol.1968.sp008455
  • Fukushima, K., Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position, Biological Cybernetics, 36 (1980), 193-202.
  • Bilbrey, J. A., Heindel, J. P., Schram, M., Bandyopadhyay, P., Xantheas, S. S., Choudhury, S., A look inside the black box: Using graph-theoretical descriptors to interpret a continuous-filter convolutional neural network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters, The Journal of Chemical Physics, 153(2) (2020), 024302.https://doi.org/10.1063/5.0009933
  • Baloglu, U. B., Talo, M., Yildirim, O., San Tan, R., Acharya, U. R., Classification of myocardial infarction with multi-lead ECG signals and deep CNN, Pattern Recognition Letters, 122 (2019), 23-30. https://doi.org/10.1016/j.patrec.2019.02.016
  • Acharya, U. R., Fujita, H., Oh SL., Hagiwara, Y., Tan, J. H., Adam, M., Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals, Information Sciences, 415 (2017), 190-8. https://doi.org/10.1016/j.ins.2017.06.027
  • Lee, H. K., Choi, Y. S., Application of continuous wavelet transform and convolutional neural network in decoding motor imagery brain-computer interface, Entropy, 21(12) (2019), 1199. https://doi.org/10.3390/e21121199
  • Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., Mobilenets: Efficient convolutional neural networks for mobile vision applications, arXiv preprint arXiv:170404861, (2017). https://doi.org/10.48550/arXiv.1704.04861
  • Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L. C., Mobilenetv2: Inverted residuals and linear bottlenecks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018).
  • Sutskever, I., Martens, J., Dahl, G., Hinton, G., On the importance of initialization and momentum in deep learning, International Conference on Machine Learning, (2013) PMLR. https://proceedings.mlr.press/v28/sutskever13.html
  • Yang, J., Yang, G., Modified convolutional neural network based on dropout and the stochastic gradient descent optimizer, Algorithms, 11(3) (2018), 28. https://doi.org/10.3390/a11030028
  • Kingma, D. P., Ba, J., Adam: A method for stochastic optimization, arXiv preprint arXiv:14126980, (2014). https://doi.org/10.48550/arXiv.1412.6980
  • Tieleman, T., Hinton, G., Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude, Coursera: Neural networks for machine learning, 4(2) (2012), 26-31.
  • McHugh, M. L., The chi-square test of independence, Biochemia Medica, 23(2) (2013), 143-9. https://doi.org/10.11613/BM.2013.018
  • Sharpe, D., Chi-square test is statistically significant: Now what?, Practical Assessment, Research, and Evaluation, 20(1) (2015), 8. https://doi.org/10.7275/tbfa-x148
  • Sankaran, M., Approximations to the non-central chi-square distribution, Biometrika, 50(1/2) (1963), 199-204.
  • Widodo, A., Yang, B-S., Support vector machine in machine condition monitoring and fault diagnosis, Mechanical Systems and Signal Processing, 21(6) (2007), 2560-74. https://doi.org/10.1016/j.ymssp.2006.12.007
  • Das, A., Rad, P., Opportunities and challenges in explainable artificial intelligence (xai): A survey, arXiv preprint arXiv:200611371, (2020). https://doi.org/10.48550/arXiv.2006.11371
  • Kaggle, Monkeypox Skin Dataset 2022 [Available from: https://www.kaggle.com/datasets/nafin59/monkeypox-skin-lesion-dataset].
  • Sharifrazi, D., Alizadehsani, R., Roshanzamir, M., Joloudari, J. H., Shoeibi, A., Jafari, M., Hussain, S., Sani, Z. A., Hasanzadeh, F., Khozeimeh, F., Khosravi, A., Nahavandi, S., Panahiazar, M., Zare, A., Islam, S. M. S., Acharya, U. R., Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images, Biomedical Signal Processing and Control, 68 (2021), 102622. https://doi.org/10.1016/j.bspc.2021.102622
  • Singh, D., Taspinar, Y. S., Kursun, R., Cinar, I., Koklu, M., Ozkan, I. A., Lee, H. N., Classification and analysis of Pistachio species with pre-trained deep learning models, Electronics, 11(7) (2022), 981. https://doi.org/10.3390/electronics11070981
  • Rajinikanth, V., Joseph Raj, A. N., Thanaraj, K. P., Naik, G. R., A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection, Applied Sciences, 10(10) (2020), 3429. https://doi.org/10.3390/app10103429
  • Taspinar, Y. S., Cinar, I., Koklu, M., Classification by a stacking model using CNN features for COVID-19 infection diagnosis, Journal of X-ray Science and Technology, (2021), 1-16. doi:10.3233/XST-211031
  • Subasi, A., Medical decision support system for diagnosis of neuromuscular disorders using DWT and fuzzy support vector machines, Computers in Biology and Medicine, 42(8) (2012), 806-15. https://doi.org/10.1016/j.compbiomed.2012.06.004
  • Lopez-del Rio, A., Nonell-Canals, A., Vidal, D., Perera-Lluna, A., Evaluation of cross validation strategies in sequence-based binding prediction using deep learning, Journal of Chemical Information and Modeling, 59(4) (2019), 1645-57. doi:10.1021/acs.jcim.8b00663
  • Saber, A., Sakr, M., Abo-Seida, O. M., Keshk, A., Chen, H., A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique, IEEE Access, 9 (2021), 71194-71209. doi:10.1109/ACCESS.2021.3079204
  • Koklu, M., Ozkan, I. A., Multiclass classification of dry beans using computer vision and machine learning techniques, Computers and Electronics in Agriculture, 174 (2020), 105507. https://doi.org/10.1016/j.compag.2020.105507
  • Arlot, S., Celisse, A., A survey of cross-validation procedures for model selection, Statistics Surveys, 4 (2010), 40-79. doi:10.1214/09-SS054
Toplam 51 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İstatistik, Olasılıksal Analiz ve Modelleme
Bölüm Research Article
Yazarlar

Öznur Özaltın 0000-0001-9841-1702

Özgür Yeniay 0000-0002-0287-4524

Yayımlanma Tarihi 23 Haziran 2023
Gönderilme Tarihi 11 Kasım 2022
Kabul Tarihi 27 Aralık 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 72 Sayı: 2

Kaynak Göster

APA Özaltın, Ö., & Yeniay, Ö. (2023). Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 72(2), 482-499. https://doi.org/10.31801/cfsuasmas.1202806
AMA Özaltın Ö, Yeniay Ö. Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. Haziran 2023;72(2):482-499. doi:10.31801/cfsuasmas.1202806
Chicago Özaltın, Öznur, ve Özgür Yeniay. “Detection of Monkeypox Disease from Skin Lesion Images Using Mobilenetv2 Architecture”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 72, sy. 2 (Haziran 2023): 482-99. https://doi.org/10.31801/cfsuasmas.1202806.
EndNote Özaltın Ö, Yeniay Ö (01 Haziran 2023) Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 72 2 482–499.
IEEE Ö. Özaltın ve Ö. Yeniay, “Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture”, Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., c. 72, sy. 2, ss. 482–499, 2023, doi: 10.31801/cfsuasmas.1202806.
ISNAD Özaltın, Öznur - Yeniay, Özgür. “Detection of Monkeypox Disease from Skin Lesion Images Using Mobilenetv2 Architecture”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 72/2 (Haziran 2023), 482-499. https://doi.org/10.31801/cfsuasmas.1202806.
JAMA Özaltın Ö, Yeniay Ö. Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2023;72:482–499.
MLA Özaltın, Öznur ve Özgür Yeniay. “Detection of Monkeypox Disease from Skin Lesion Images Using Mobilenetv2 Architecture”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, c. 72, sy. 2, 2023, ss. 482-99, doi:10.31801/cfsuasmas.1202806.
Vancouver Özaltın Ö, Yeniay Ö. Detection of monkeypox disease from skin lesion images using Mobilenetv2 architecture. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2023;72(2):482-99.

Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics.

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