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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

C. S. S. Anupama1, L. Natrayan2, E. Laxmi Lydia3, Abdul Rahaman Wahab Sait4, José Escorcia-Gutierrez5, Margarita Gamarra6,*, Romany F. Mansour7

1 Department of Electronics & Instrumentation Engineering, V. R. Siddhartha Engineering College, Vijayawada, 520007, India
2 Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, Chennai, 602105, India
3 Department of Computer Science and Engineering, Vignan's Institute of Information Technology, Visakhapatnam, 530049, India
4 Department of Archives and Communication, King Faisal University, 31982, Kingdom of Saudi Arabia
5 Electronic and Telecommunicacions Program, Universidad Autónoma del Caribe, Barranquilla, 08001, Colombia
6 Department of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla, 08001, Colombia
7 Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, 72511, Egypt

* Corresponding Author: Margarita Gamarra. Email: email

Computers, Materials & Continua 2022, 70(1), 1297-1313. https://doi.org/10.32604/cmc.2022.018396

Abstract

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.

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APA Style
Anupama, C.S.S., Natrayan, L., Lydia, E.L., Sait, A.R.W., Escorcia-Gutierrez, J. et al. (2022). Deep learning with backtracking search optimization based skin lesion diagnosis model. Computers, Materials & Continua, 70(1), 1297-1313. https://doi.org/10.32604/cmc.2022.018396
Vancouver Style
Anupama CSS, Natrayan L, Lydia EL, Sait ARW, Escorcia-Gutierrez J, Gamarra M, et al. Deep learning with backtracking search optimization based skin lesion diagnosis model. Comput Mater Contin. 2022;70(1):1297-1313 https://doi.org/10.32604/cmc.2022.018396
IEEE Style
C.S.S. Anupama et al., "Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model," Comput. Mater. Contin., vol. 70, no. 1, pp. 1297-1313. 2022. https://doi.org/10.32604/cmc.2022.018396

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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