Open Access iconOpen Access

ARTICLE

crossmark

Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation

G. Murugesan1,*, J. Jeyapriya2, M. Hemalatha3, S. Rajeshkannan4

1 Department of Computer Science and Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India
2 Department of Computer Science and Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, Tamil Nadu, India
3 Department of Artificial Intelligence and Data Science, Saveetha Engineering College (Autonomous), Chennai, Tamil Nadu, India
4 Department of Electronics and Communication Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India

* Corresponding Author: G. Murugesan. Email: email

Intelligent Automation & Soft Computing 2023, 36(2), 2099-2109. https://doi.org/10.32604/iasc.2023.032068

Abstract

The diagnostic interpretation of dermoscopic images is a complex task as it is very difficult to identify the skin lesions from the normal. Thus the accurate detection of potential abnormalities is required for patient monitoring and effective treatment. In this work, a Two-Tier Segmentation (TTS) system is designed, which combines the unsupervised and supervised techniques for skin lesion segmentation. It comprises preprocessing by the median filter, TTS by Colour K-Means Clustering (CKMC) for initial segmentation and Faster Region based Convolutional Neural Network (FR-CNN) for refined segmentation. The CKMC approach is evaluated using the different number of clusters (k = 3, 5, 7, and 9). An inception network with batch normalization is employed to segment melanoma regions effectively. Different loss functions such as Mean Absolute Error (MAE), Cross Entropy Loss (CEL), and Dice Loss (DL) are utilized for performance evaluation of the TTS system. The anchor box technique is employed to detect the melanoma region effectively. The TTS system is evaluated using 200 dermoscopic images from the PH2 database. The segmentation accuracies are analyzed in terms of Pixel Accuracy (PA) and Jaccard Index (JI). Results show that the TTS system has 90.19% PA with 0.8048 JI for skin lesion segmentation using DL in FR-CNN with seven clusters in CKMC than CEL and MAE.

Keywords


Cite This Article

APA Style
Murugesan, G., Jeyapriya, J., Hemalatha, M., Rajeshkannan, S. (2023). Faster region based convolutional neural network for skin lesion segmentation. Intelligent Automation & Soft Computing, 36(2), 2099-2109. https://doi.org/10.32604/iasc.2023.032068
Vancouver Style
Murugesan G, Jeyapriya J, Hemalatha M, Rajeshkannan S. Faster region based convolutional neural network for skin lesion segmentation. Intell Automat Soft Comput . 2023;36(2):2099-2109 https://doi.org/10.32604/iasc.2023.032068
IEEE Style
G. Murugesan, J. Jeyapriya, M. Hemalatha, and S. Rajeshkannan "Faster Region Based Convolutional Neural Network for Skin Lesion Segmentation," Intell. Automat. Soft Comput. , vol. 36, no. 2, pp. 2099-2109. 2023. https://doi.org/10.32604/iasc.2023.032068



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.
  • 715

    View

  • 432

    Download

  • 0

    Like

Share Link