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Dermatoscopy Using Multi-Layer Perceptron, Convolution Neural Network, and Capsule Network to Differentiate Malignant Melanoma From Benign Nevus

Dermatoscopy Using Multi-Layer Perceptron, Convolution Neural Network, and Capsule Network to Differentiate Malignant Melanoma From Benign Nevus

Shamik Tiwari
Copyright: © 2021 |Volume: 16 |Issue: 3 |Pages: 16
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781799859802|DOI: 10.4018/IJHISI.20210701.oa4
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MLA

Tiwari, Shamik. "Dermatoscopy Using Multi-Layer Perceptron, Convolution Neural Network, and Capsule Network to Differentiate Malignant Melanoma From Benign Nevus." IJHISI vol.16, no.3 2021: pp.58-73. http://doi.org/10.4018/IJHISI.20210701.oa4

APA

Tiwari, S. (2021). Dermatoscopy Using Multi-Layer Perceptron, Convolution Neural Network, and Capsule Network to Differentiate Malignant Melanoma From Benign Nevus. International Journal of Healthcare Information Systems and Informatics (IJHISI), 16(3), 58-73. http://doi.org/10.4018/IJHISI.20210701.oa4

Chicago

Tiwari, Shamik. "Dermatoscopy Using Multi-Layer Perceptron, Convolution Neural Network, and Capsule Network to Differentiate Malignant Melanoma From Benign Nevus," International Journal of Healthcare Information Systems and Informatics (IJHISI) 16, no.3: 58-73. http://doi.org/10.4018/IJHISI.20210701.oa4

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Abstract

Epiluminescence microscopy, more simply, dermatoscopy, entails a process using imaging to examine skin lesions. Various sorts of skin ailments, for example, melanoma, may be differentiated via these skin images. With the adverse possibilities of malignant melanoma causing death, an early diagnosis of melanoma can impact on the survival, length, and quality of life of the affected victim. Image recognition-based detection of different tissue classes is significant to implementing computer-aided diagnosis via histological images. Conventional image recognition require handcrafted feature extraction before the application of machine learning. Today, deep learning is offering significant choices with the progression of artificial learning to defeat the complications of the handcrafted feature extraction methods. A deep learning-based approach for the recognition of melanoma via the Capsule network is proposed here. The novel approach is compared with a multi-layer perceptron and convolution network with the Capsule network model yielding the classification accuracy at 98.9%.