Open Access

ARTICLE

IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction

Tanzila Saba¹, Rabia Javed^2,3, Mohd Shafry Mohd Rahim², Amjad Rehman^1,*, Saeed Ali Bahaj⁴

1 Artificial Intelligence & Data Analytics Lab CCIS, Prince Sultan University, Riyadh, 11586, Saudi Arabia
2 School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, 81310, Johor Bahru, Malaysia
3 Department of Computer Science, Lahore College for Women University, Lahore, 54000, Pakistan
4 MIS Department College of Business Administration, Prince Sattam bin Abdulaziz University, Alkharj, 11942, Saudi Arabia

* Corresponding Author: Amjad Rehman. Email:

(This article belongs to the Special Issue: Pervasive Computing and Communication: Challenges, Technologies & Opportunities)

Computers, Materials & Continua 2022, 71(3), 6219-6237. https://doi.org/10.32604/cmc.2022.020865

Received 11 June 2021; Accepted 25 October 2021; Issue published 14 January 2022

Abstract

The Internet of Medical Things (IoMT) and cloud-based healthcare applications, services are beneficial for better decision-making in recent years. Melanoma is a deadly cancer with a higher mortality rate than other skin cancer types such as basal cell, squamous cell, and Merkel cell. However, detection and treatment at an early stage can result in a higher chance of survival. The classical methods of detection are expensive and labor-intensive. Also, they rely on a trained practitioner's level, and the availability of the needed equipment is essential for the early detection of Melanoma. The current improvement in computer-aided systems is providing very encouraging results in terms of precision and effectiveness. In this article, we propose an improved region growing technique for efficient extraction of the lesion boundary. This analysis and detection of Melanoma are helpful for the expert dermatologist. The CNN features are extracted using the pre-trained VGG-19 deep learning model. In the end, the selected features are classified by SVM. The proposed technique is gauged on openly accessible two datasets ISIC 2017 and PH2. For the evaluation of our proposed framework, qualitative and quantitative experiments are performed. The suggested segmentation method has provided encouraging statistical results of Jaccard index 0.94, accuracy 95.7% on ISIC 2017, and Jaccard index 0.91, accuracy 93.3% on the PH2 dataset. These results are notably better than the results of prevalent methods available on the same datasets. The machine learning SVM classifier executes significantly well on the suggested feature vector, and the comparative analysis is carried out with existing methods in terms of accuracy. The proposed method detects and classifies melanoma far better than other methods. Besides, our framework gained promising results in both segmentation and classification phases.

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