Deep learning with backtracking search optimization based skin lesion diagnosis model
Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized in the development of smart healthcare systems. These entities can support...
- Autores:
-
Anupama, C. S. S.
Natrayan, L.
Laxmi Lydia, E.
Wahab Sait, Abdul Rahaman
Escorcia-Gutierrez, Jose
Gamarra, Margarita
Mansour, Romany F.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9058
- Acceso en línea:
- https://hdl.handle.net/11323/9058
https://repositorio.cuc.edu.co/
- Palabra clave:
- Intelligent models
Skin lesion
Dermoscopic images
Smart healthcare
Internet of things
- Rights
- openAccess
- License
- Copyright© 2020 Tech Science Press
| id |
RCUC2_34c7e6d35990838c06e4c47aa559c9c2 |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/9058 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.eng.fl_str_mv |
Deep learning with backtracking search optimization based skin lesion diagnosis model |
| title |
Deep learning with backtracking search optimization based skin lesion diagnosis model |
| spellingShingle |
Deep learning with backtracking search optimization based skin lesion diagnosis model Intelligent models Skin lesion Dermoscopic images Smart healthcare Internet of things |
| title_short |
Deep learning with backtracking search optimization based skin lesion diagnosis model |
| title_full |
Deep learning with backtracking search optimization based skin lesion diagnosis model |
| title_fullStr |
Deep learning with backtracking search optimization based skin lesion diagnosis model |
| title_full_unstemmed |
Deep learning with backtracking search optimization based skin lesion diagnosis model |
| title_sort |
Deep learning with backtracking search optimization based skin lesion diagnosis model |
| dc.creator.fl_str_mv |
Anupama, C. S. S. Natrayan, L. Laxmi Lydia, E. Wahab Sait, Abdul Rahaman Escorcia-Gutierrez, Jose Gamarra, Margarita Mansour, Romany F. |
| dc.contributor.author.spa.fl_str_mv |
Anupama, C. S. S. Natrayan, L. Laxmi Lydia, E. Wahab Sait, Abdul Rahaman Escorcia-Gutierrez, Jose Gamarra, Margarita Mansour, Romany F. |
| dc.subject.proposal.eng.fl_str_mv |
Intelligent models Skin lesion Dermoscopic images Smart healthcare Internet of things |
| topic |
Intelligent models Skin lesion Dermoscopic images Smart healthcare Internet of things |
| description |
Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized in the development of smart healthcare systems. These entities can support real-time applications by exploiting massive volumes of data, produced by wearable sensor devices. The advent of evolutionary computation algorithms and Deep Learning (DL) models has gained significant attention in healthcare diagnosis, especially in decision making process. Skin cancer is the deadliest disease which affects people across the globe. Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions. The current research article presents a new skin lesion diagnosis model i.e., Deep Learning with Evolutionary Algorithm based Image Segmentation (DL-EAIS) for IoT and cloud-based smart healthcare environments. Primarily, the dermoscopic images are captured using IoT devices, which are then transmitted to cloud servers for further diagnosis. Besides, Backtracking Search optimization Algorithm (BSA) with Entropy-Based Thresholding (EBT) i.e., BSA-EBT technique is applied in image segmentation. Followed by, Shallow Convolutional Neural Network (SCNN) model is utilized as a feature extractor. In addition, Deep-Kernel Extreme Learning Machine (D-KELM) model is employed as a classification model to determine the class labels of dermoscopic images. An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset. The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures. |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021-09-07 |
| dc.date.accessioned.none.fl_str_mv |
2022-03-08T16:14:20Z |
| dc.date.available.none.fl_str_mv |
2022-03-08T16:14:20Z |
| dc.type.spa.fl_str_mv |
Artículo de revista |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.content.spa.fl_str_mv |
Text |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| format |
http://purl.org/coar/resource_type/c_6501 |
| status_str |
acceptedVersion |
| dc.identifier.citation.spa.fl_str_mv |
S., C., Natrayan, L., Lydia, E. L., Rahaman, A., Escorcia-Gutierrez, J. et al. (2022). Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model. CMC-Computers, Materials & Continua, 70(1), 1297–1313. |
| dc.identifier.issn.spa.fl_str_mv |
1546-2218 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/9058 |
| dc.identifier.doi.spa.fl_str_mv |
10.32604/cmc.2022.018396 |
| dc.identifier.eissn.spa.fl_str_mv |
1546-2226 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
S., C., Natrayan, L., Lydia, E. L., Rahaman, A., Escorcia-Gutierrez, J. et al. (2022). Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model. CMC-Computers, Materials & Continua, 70(1), 1297–1313. 1546-2218 10.32604/cmc.2022.018396 1546-2226 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/9058 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartofjournal.spa.fl_str_mv |
Computers, Materials and Continua |
| dc.relation.references.spa.fl_str_mv |
[1] T. Y. Satheesha, D. Satyanarayana, M. N. Giriprasad and K. N. Nagesh, “Detection of melanoma using distinct features,” in 2016 3rd MEC Int. Conf. on Big Data and Smart City, IEEE, Muscat, Oman, pp. 1–6, 2016. [2] R. S. Sundar and M. Vadivel, “Performance analysis of melanoma early detection using skin lession classification system,” in 2016 Int. Conf. on Circuit, Power and Computing Technologies, IEEE, Nagercoil, India, pp. 1–5, 2016. [3] S. M. Kumar, J. R. Kumar and K. Gopalakrishnan, “Skin cancer diagnostic using machine learning techniques-shearlet transform and naïve Bayes classifier,” International Journal of Engineering and Advanced Technology, vol. 9, no. 2, pp. 3478–3480, 2019. [4] C. Spampinato, B. Boom and J. He, “First international workshop on visual interfaces for ground truth collection in computer vision applications,” in AVI ‘12: Proc. of the Int. Working Conf. on Advanced Visual Interfaces, Capri Island, Italy, pp. 812–814, 2012. [5] C. Sinz, P. Tschandl, C. Rosendahl, B. N. Akay, G. Argenziano et al., “Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin,” Journal of the American Academy of Dermatology, vol. 77, no. 6, pp. 1100–1109, 2017. [6] K. Shankar, Y. Zhang, Y. Liu, L. Wu and C. H. Chen, “Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification,” IEEE Access, vol. 8, pp. 118164–118173, 2020. [7] I. V. Pustokhina, D. A. Pustokhin, D. Gupta, A. Khanna, K. Shankar et al., “An effective training scheme for deep neural network in edge computing enabled internet of medical things (IoMT) systems,” IEEE Access, vol. 8, pp. 107112–107123, 2020. [8] R. J. S. Raj, S. J. Shobana, I. V. Pustokhina, D. A. Pustokhin, D. Gupta et al., “Optimal feature selection-based medical image classification using deep learning model in internet of medical things,” IEEE Access, vol. 8, pp. 58006–58017, 2020. [9] S. Kathiresan, A. R. W. Sait, D. Gupta, S. K. Lakshmanaprabu, A. Khanna et al., “Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model,” Pattern Recognition Letters, vol. 133, pp. 210–216, 2020. [10] Z. Yu, X. Jiang, F. Zhou, J. Qin, D. Ni et al., “Melanoma recognition in dermoscopy images via aggregated deep convolutional features,” IEEE Transactions on Biomedical Engineering, vol. 66, pp. 1006– 1016, 2018. [11] N. C. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti et al., “Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI),” in Proc. 15th Int. Symp. on Biomedical Imaging, IEEE, Washington, DC, USA, pp. 4–7, 2018. [12] Y. Yuan, M. Chao and Y. C. Lo, “Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance,” IEEE Transactions on Medical Imaging, vol. 36, pp. 1876–1886, 2017. [13] L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham et al., “Dermoscopic image segmentation via multi-stage fully convolutional networks,” IEEE Transactions on Biomedical Engineering, vol. 64, pp. 2065–2074, 2017. [14] O. Abuzaghleh, B. D. Barkana and M. Faezipour, “Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 3, pp. 1–12, 2015. [15] T. Do, T. Hoang, V. Pomponiu, Y. Zhou, Z. Chen et al., “Accessible melanoma detection using smartphones and mobile image analysis,” IEEE Transactions on Multimedia, vol. 20, no. 10, pp. 2849–2864, 2018. [16] P. Sahu, D. Yu and H. Qin, “Apply lightweight deep learning on internet of things for low-cost and easy-to-access skin cancer detection,” in Proc.: SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, Houston, Texas, United States, 2018. [17] M. Y. Sikkandar, B. A. Alrasheadi, N. B. Prakash, G. R. Hemalakshmi, A. Mohanarathinam et al., “Deep learning based an automated skin lesion segmentation and intelligent classification model,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 1–11, 2020. [18] K. J. Jaworek, “Computer-aided diagnosis of micro-malignant melanoma lesions applying support vector machines,” BioMed Research International, vol. 2016, pp. 1–8, 2016. [19] S. Mishra and M. Panda, “Bat algorithm for multilevel colour image segmentation using entropy-based thresholding,” Arabian Journal for Science and Engineering, vol. 43, no. 12, pp. 7285–7314, 2018. [20] S. Pare, A. Kumar, V. Bajaj and G. K. Singh, “A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve,” Applied Soft Computing, vol. 47, pp. 76–102, 2016. [21] P. Civicioglu, “Backtracking search optimization algorithm for numerical optimization problems,” Applied Mathematics and Computation, vol. 219, no. 15, pp. 8121–8144, 2013. [22] K. Guney, A. Durmus and S. Basbug, “Backtracking search optimization algorithm for synthesis of concentric circular antenna arrays,” International Journal of Antennas and Propagation, vol. 2014, pp. 1–11, 2014. [23] A. Ghoneim, G. Muhammad and M. S. Hossain, “Cervical cancer classification using convolutional neural networks and extreme learning machines,” Future Generation Computer Systems, vol. 102, pp.643–649, 2020. [24] J. Li, B. Xi, Q. Du, R. Song, Y. Li et al., “Deep kernel extreme-learning machine for the spectral–spatial classification of hyperspectral imagery,” Remote Sensing, vol. 10, no. 12, pp. 2036, 2018. [25] J. Tang, C. Deng and G. Huang, “Extreme learning machine for multilayer perceptron,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, pp. 809–821, 2015. [26] Y. Yuan and Y. C. Lo, “Improving dermoscopic image segmentation with enhanced convolutionaldeconvolutional networks,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 519–526, 2019. [27] Y. Li and L. Shen, “Skin lesion analysis towards melanoma detection using deep learning network,” Sensors, vol. 18, no. 2, pp. 556, 2018. [28] L. Bi, J. Kim, E. Ahn and D. Feng, “Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks,” arXiv, vol. 2017, pp. 1–4, arXiv: 1703.04197, 2017. [29] H. M. Ünver and E. Ayan, “Skin lesion segmentation in dermoscopic images with combination of YOLO and grabcut algorithm,” Diagnostics, vol. 9, no. 3, pp. 72, 2019. [30] D. Połap, A. Winnicka, K. Serwata, K. Ke˛sik and M. Wozniak, “An intelligent system for monitoring ´ skin diseases,” Sensors, vol. 18, no. 8, pp. 2552, 2018. [31] T. Y. Tan, L. Zhang and C. P. Lim, “Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks,” Knowledge-Based Systems, vol. 187, pp. 104807, 2020. |
| dc.relation.citationendpage.spa.fl_str_mv |
1313 |
| dc.relation.citationstartpage.spa.fl_str_mv |
1297 |
| dc.relation.citationissue.spa.fl_str_mv |
1 |
| dc.relation.citationvolume.spa.fl_str_mv |
70 |
| dc.rights.spa.fl_str_mv |
Copyright© 2020 Tech Science Press Atribución 4.0 Internacional (CC BY 4.0) |
| dc.rights.uri.spa.fl_str_mv |
https://creativecommons.org/licenses/by/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Copyright© 2020 Tech Science Press Atribución 4.0 Internacional (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.spa.fl_str_mv |
17 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Tech Science Press |
| dc.publisher.place.spa.fl_str_mv |
United States |
| institution |
Corporación Universidad de la Costa |
| dc.source.url.spa.fl_str_mv |
https://www.techscience.com/cmc/v70n1/44352 |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/b57ed69b-4308-4021-9eef-d1a15ff6d0cb/download https://repositorio.cuc.edu.co/bitstreams/b7b6212d-c120-4e27-89e8-cfbb94dac75c/download https://repositorio.cuc.edu.co/bitstreams/6915592c-70a8-4edb-ae30-918384e3226b/download https://repositorio.cuc.edu.co/bitstreams/40e09a22-5ef5-468c-be10-6a5bde317b8b/download |
| bitstream.checksum.fl_str_mv |
062310e153a5aa5124de8d6d9a690574 e30e9215131d99561d40d6b0abbe9bad cf688c1e01e61917361c509f274f92c8 708407d4fa78ff1d8bfce724650bd09b |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1811760679805255680 |
| spelling |
Anupama, C. S. S.Natrayan, L.Laxmi Lydia, E.Wahab Sait, Abdul RahamanEscorcia-Gutierrez, JoseGamarra, MargaritaMansour, Romany F.2022-03-08T16:14:20Z2022-03-08T16:14:20Z2021-09-07S., C., Natrayan, L., Lydia, E. L., Rahaman, A., Escorcia-Gutierrez, J. et al. (2022). Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model. CMC-Computers, Materials & Continua, 70(1), 1297–1313.1546-2218https://hdl.handle.net/11323/905810.32604/cmc.2022.0183961546-2226Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized in the development of smart healthcare systems. These entities can support real-time applications by exploiting massive volumes of data, produced by wearable sensor devices. The advent of evolutionary computation algorithms and Deep Learning (DL) models has gained significant attention in healthcare diagnosis, especially in decision making process. Skin cancer is the deadliest disease which affects people across the globe. Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions. The current research article presents a new skin lesion diagnosis model i.e., Deep Learning with Evolutionary Algorithm based Image Segmentation (DL-EAIS) for IoT and cloud-based smart healthcare environments. Primarily, the dermoscopic images are captured using IoT devices, which are then transmitted to cloud servers for further diagnosis. Besides, Backtracking Search optimization Algorithm (BSA) with Entropy-Based Thresholding (EBT) i.e., BSA-EBT technique is applied in image segmentation. Followed by, Shallow Convolutional Neural Network (SCNN) model is utilized as a feature extractor. In addition, Deep-Kernel Extreme Learning Machine (D-KELM) model is employed as a classification model to determine the class labels of dermoscopic images. An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset. The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.17 páginasapplication/pdfengTech Science PressUnited StatesCopyright© 2020 Tech Science PressAtribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Deep learning with backtracking search optimization based skin lesion diagnosis modelArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionhttps://www.techscience.com/cmc/v70n1/44352Computers, Materials and Continua[1] T. Y. Satheesha, D. Satyanarayana, M. N. Giriprasad and K. N. Nagesh, “Detection of melanoma using distinct features,” in 2016 3rd MEC Int. Conf. on Big Data and Smart City, IEEE, Muscat, Oman, pp. 1–6, 2016.[2] R. S. Sundar and M. Vadivel, “Performance analysis of melanoma early detection using skin lession classification system,” in 2016 Int. Conf. on Circuit, Power and Computing Technologies, IEEE, Nagercoil, India, pp. 1–5, 2016.[3] S. M. Kumar, J. R. Kumar and K. Gopalakrishnan, “Skin cancer diagnostic using machine learning techniques-shearlet transform and naïve Bayes classifier,” International Journal of Engineering and Advanced Technology, vol. 9, no. 2, pp. 3478–3480, 2019.[4] C. Spampinato, B. Boom and J. He, “First international workshop on visual interfaces for ground truth collection in computer vision applications,” in AVI ‘12: Proc. of the Int. Working Conf. on Advanced Visual Interfaces, Capri Island, Italy, pp. 812–814, 2012.[5] C. Sinz, P. Tschandl, C. Rosendahl, B. N. Akay, G. Argenziano et al., “Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin,” Journal of the American Academy of Dermatology, vol. 77, no. 6, pp. 1100–1109, 2017.[6] K. Shankar, Y. Zhang, Y. Liu, L. Wu and C. H. Chen, “Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification,” IEEE Access, vol. 8, pp. 118164–118173, 2020.[7] I. V. Pustokhina, D. A. Pustokhin, D. Gupta, A. Khanna, K. Shankar et al., “An effective training scheme for deep neural network in edge computing enabled internet of medical things (IoMT) systems,” IEEE Access, vol. 8, pp. 107112–107123, 2020.[8] R. J. S. Raj, S. J. Shobana, I. V. Pustokhina, D. A. Pustokhin, D. Gupta et al., “Optimal feature selection-based medical image classification using deep learning model in internet of medical things,” IEEE Access, vol. 8, pp. 58006–58017, 2020.[9] S. Kathiresan, A. R. W. Sait, D. Gupta, S. K. Lakshmanaprabu, A. Khanna et al., “Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model,” Pattern Recognition Letters, vol. 133, pp. 210–216, 2020.[10] Z. Yu, X. Jiang, F. Zhou, J. Qin, D. Ni et al., “Melanoma recognition in dermoscopy images via aggregated deep convolutional features,” IEEE Transactions on Biomedical Engineering, vol. 66, pp. 1006– 1016, 2018.[11] N. C. Codella, D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti et al., “Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI),” in Proc. 15th Int. Symp. on Biomedical Imaging, IEEE, Washington, DC, USA, pp. 4–7, 2018.[12] Y. Yuan, M. Chao and Y. C. Lo, “Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance,” IEEE Transactions on Medical Imaging, vol. 36, pp. 1876–1886, 2017.[13] L. Bi, J. Kim, E. Ahn, A. Kumar, M. Fulham et al., “Dermoscopic image segmentation via multi-stage fully convolutional networks,” IEEE Transactions on Biomedical Engineering, vol. 64, pp. 2065–2074, 2017.[14] O. Abuzaghleh, B. D. Barkana and M. Faezipour, “Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 3, pp. 1–12, 2015.[15] T. Do, T. Hoang, V. Pomponiu, Y. Zhou, Z. Chen et al., “Accessible melanoma detection using smartphones and mobile image analysis,” IEEE Transactions on Multimedia, vol. 20, no. 10, pp. 2849–2864, 2018.[16] P. Sahu, D. Yu and H. Qin, “Apply lightweight deep learning on internet of things for low-cost and easy-to-access skin cancer detection,” in Proc.: SPIE 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, Houston, Texas, United States, 2018.[17] M. Y. Sikkandar, B. A. Alrasheadi, N. B. Prakash, G. R. Hemalakshmi, A. Mohanarathinam et al., “Deep learning based an automated skin lesion segmentation and intelligent classification model,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 1–11, 2020.[18] K. J. Jaworek, “Computer-aided diagnosis of micro-malignant melanoma lesions applying support vector machines,” BioMed Research International, vol. 2016, pp. 1–8, 2016.[19] S. Mishra and M. Panda, “Bat algorithm for multilevel colour image segmentation using entropy-based thresholding,” Arabian Journal for Science and Engineering, vol. 43, no. 12, pp. 7285–7314, 2018.[20] S. Pare, A. Kumar, V. Bajaj and G. K. Singh, “A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve,” Applied Soft Computing, vol. 47, pp. 76–102, 2016.[21] P. Civicioglu, “Backtracking search optimization algorithm for numerical optimization problems,” Applied Mathematics and Computation, vol. 219, no. 15, pp. 8121–8144, 2013.[22] K. Guney, A. Durmus and S. Basbug, “Backtracking search optimization algorithm for synthesis of concentric circular antenna arrays,” International Journal of Antennas and Propagation, vol. 2014, pp. 1–11, 2014.[23] A. Ghoneim, G. Muhammad and M. S. Hossain, “Cervical cancer classification using convolutional neural networks and extreme learning machines,” Future Generation Computer Systems, vol. 102, pp.643–649, 2020.[24] J. Li, B. Xi, Q. Du, R. Song, Y. Li et al., “Deep kernel extreme-learning machine for the spectral–spatial classification of hyperspectral imagery,” Remote Sensing, vol. 10, no. 12, pp. 2036, 2018.[25] J. Tang, C. Deng and G. Huang, “Extreme learning machine for multilayer perceptron,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, pp. 809–821, 2015.[26] Y. Yuan and Y. C. Lo, “Improving dermoscopic image segmentation with enhanced convolutionaldeconvolutional networks,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 2, pp. 519–526, 2019.[27] Y. Li and L. Shen, “Skin lesion analysis towards melanoma detection using deep learning network,” Sensors, vol. 18, no. 2, pp. 556, 2018.[28] L. Bi, J. Kim, E. Ahn and D. Feng, “Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks,” arXiv, vol. 2017, pp. 1–4, arXiv: 1703.04197, 2017.[29] H. M. Ünver and E. Ayan, “Skin lesion segmentation in dermoscopic images with combination of YOLO and grabcut algorithm,” Diagnostics, vol. 9, no. 3, pp. 72, 2019.[30] D. Połap, A. Winnicka, K. Serwata, K. Ke˛sik and M. Wozniak, “An intelligent system for monitoring ´ skin diseases,” Sensors, vol. 18, no. 8, pp. 2552, 2018.[31] T. Y. Tan, L. Zhang and C. P. Lim, “Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks,” Knowledge-Based Systems, vol. 187, pp. 104807, 2020.13131297170Intelligent modelsSkin lesionDermoscopic imagesSmart healthcareInternet of thingsPublicationORIGINALDeep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model.pdfDeep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model.pdfapplication/pdf1255243https://repositorio.cuc.edu.co/bitstreams/b57ed69b-4308-4021-9eef-d1a15ff6d0cb/download062310e153a5aa5124de8d6d9a690574MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/b7b6212d-c120-4e27-89e8-cfbb94dac75c/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTDeep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model.pdf.txtDeep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model.pdf.txttext/plain39499https://repositorio.cuc.edu.co/bitstreams/6915592c-70a8-4edb-ae30-918384e3226b/downloadcf688c1e01e61917361c509f274f92c8MD53THUMBNAILDeep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model.pdf.jpgDeep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model.pdf.jpgimage/jpeg12955https://repositorio.cuc.edu.co/bitstreams/40e09a22-5ef5-468c-be10-6a5bde317b8b/download708407d4fa78ff1d8bfce724650bd09bMD5411323/9058oai:repositorio.cuc.edu.co:11323/90582024-09-16 16:45:52.894https://creativecommons.org/licenses/by/4.0/Copyright© 2020 Tech Science Pressopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |