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%.
Highlights
Malignant melanoma is a form of human skin cancer arising from the melanocytes
This paper addresses the issue in investigating the performance of three different classification models (i.e., multi-layer perceptron (MLP), CNN and Capsule Network (CapsNet)) for the categorization of melanoma and nevus skin lesions
This study reported on the application of three different classification models namely multi-layer perceptron, convolution neural network and capsule network to distinguish malignant melanoma from benign nevus
Summary
Malignant melanoma (or, melanoma) is a form of human skin cancer arising from the melanocytes (pigment-containing cells). Appearing on the legs of women and backs of the men, the affected cells can grow from a mole with some changes such as an escalation in size, uneven edges, color change, irritation, and skin breakdown. As the use of conventional approaches is often costly and time-consuming with biopsy tests often having several side effects, nowadays, image analysis and machine learning based systems are highly recommended for melanoma detection via dermoscopic images (Singhal et al, 2015; Ker et al, 2018). The limited availability of trained dermatologists has deterred the use of conventional melanoma detection methods. The final section (Section 5) provides concluding remarks relating to the key contributions, limitations, practical implications and potential future work
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