Performance and Analysis of a U-Net Model for Automated Skin Lesion Segmentation

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Published: Oct 7, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i11s.8181
Keywords:
Accuracy, Dice Coefficient, ReLU, Segmentataion, Skin Lesion, U Net

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Sheetal Nana Patil
Research Scholar, Department of Computer Engineering, Shri Shivaji Vidya Prasarak Sanstha's Bapusaheb Shivajirao Deore College of Engineering, Dhule (MS), India
Hitendra D. Patil
Professor , Department of Computer Engineering, Shri Shivaji Vidya Prasarak Sanstha's Bapusaheb Shivajirao Deore College of Engineering, Dhule (MS), India

Abstract

A greater proportion of people are affected by skin cancer, particularly melanoma, which has a higher tendency to metastasize. For Dermatologist, Visual inspections are most challenging & complex task for melanoma detection. To solve this problem, dermoscopic images are analyzed and segmented. Due to the sensitivity involved in surgical operations, existing techniques are unable to achieve higher accuracy. As a result, computer-aided systems are essential to detect & segment dermoscopic images.


    In this paper, for segmentation 5000 skin images were taken from the HAM10000 dataset. Prior to segmentation, preprocessing is done by resizing images. A novel U Net structure is a fully convolutional network is presented & implemented using up-sampling and down-sampling technique with Rectified Linear Units (ReLU) for activation functions. The outcomes of proposed methodology shows performance improvement for skin-lesion segmentation with 94.7 % pixel accuracy & 89.2 % dice coefficient compared with existing KNN & SVM techniques.

Article Details

How to Cite
Patil, S. N. ., & Patil, H. D. . (2023). Performance and Analysis of a U-Net Model for Automated Skin Lesion Segmentation. International Journal on Recent and Innovation Trends in Computing and Communication, 11(11s), 508–518. https://doi.org/10.17762/ijritcc.v11i11s.8181
Issue
Vol. 11 No. 11s (2023)
Section
Articles

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