A Hybrid Automatic Classification Model for Skin Tumour Images

The skin, uniquely positioned at the interface between the human body and the external world, plays a multifaceted immunologic role in human life. In medical practice, early accurate detection of all types of skin tumours is essential to guide appropriate management and improve patients’ survival. The most important issue is to differentiate between malignant skin tumours and benign lesions. The aim of this research is the classification of skin tumours by analysing medical skin tumour dermoscopy images. This paper is focused on a new strategy based on deep convolutional neural networks which have recently shown a state-of-the-art performance to define strategy to automatic classification for skin tumour images. The proposed system is tested on well-known HAM10000 data set. For experimental results, verification is performed and the results are compared with similar researches.

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Deep Convolutional Neural Networks on Automatic Classification for Skin Tumour Images

Simić

Banković³

et al. 2021

Logic Journal of the IGPL

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A Hybrid Automatic Classification Model for Skin Tumour Images

Cited by 1 publication

References 18 publications

Deep Convolutional Neural Networks on Automatic Classification for Skin Tumour Images

Deep Convolutional Neural Networks on Automatic Classification for Skin Tumour Images

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