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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
S, Gopinath
Karpagam Institute of Technology
Computer Science & IT Electrical & Electronics Engineering

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Wn-Based Skin Cancer Lesion Segmentation of Melanoma Using Deep Learning Methods M, Jayasree; K, Kavin Kumar; Chandrasekaran, Gokul; M, Saranya; S, Gopinath; T, Rajasekaran
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 2: June 2025
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i2.6151

Abstract

The incidence rate of skin cancer, particularly malignant melanoma, has risen to high levels during the last decades. The biopsy method used for cancer treatment was found to be a painful and time-consuming one. Also, laboratory sampling of skin cancer leads to the spread of lesions to other body parts. Due to the different colours and shapes of the skin, segmentation and classification of melanoma are more challenging to analyze. An automatic method of dermoscopic skin lesion detection will be introduced. Recognizing the skin lesions at an early stage is essential for effective treatment. Proposed an efficient skin cancer image segmentation method using Fixed-Grid Wavelet Network (FGWN) and developed a novel classification method using deep learning techniques. FGWNs constitute R, G and B values of three inputs, a hidden layer and an output. Input skin cancer image is segmented, and the exact boundary is determined accordingly. The features of the segmented images were extracted using the Orthogonal Least Squares (OLS) algorithm. The AlexNet model was first used to classify pictures of melanoma cancer. Next, ResNet-50 and Ordinary Convolutional Neural Networks (CNN) was deployed. Wavelet Network (WN)-Based segmentation achieved an accuracy of 99.78% in detecting skin cancer lesion boundaries. Ordinary CNN shows an accuracy of 93.37% for 100 epochs. ResNet-50 models show 88.37% accuracy for melanoma classification. The number of training epochs and the volume of training data both impact accuracy. Deep learning algorithms can significantly improve categorization efficiency.
Co-Authors Chandrasekaran, Gokul K, Kavin Kumar M, Jayasree M, Saranya T, Rajasekaran