Open Access

ARTICLE

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 Gamarra^6,*, 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:

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

Received 07 March 2021; Accepted 01 May 2021; Issue published 07 September 2021

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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Citations