A multi-class skin Cancer classification using deep convolutional neural networks
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A multi-class skin Cancer classification using deep convolutional neural networks Saket S. Chaturvedi 1 & Jitendra V. Tembhurne 2
& Tausif Diwan
2
Received: 10 August 2019 / Revised: 22 June 2020 / Accepted: 21 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Skin Cancer accounts for one-third of all diagnosed cancers worldwide. The prevalence of skin cancers have been rising over the past decades. In recent years, use of dermoscopy has enhanced the diagnostic capability of skin cancer. The accurate diagnosis of skin cancer is challenging for dermatologists as multiple skin cancer types may appear similar in appearance. The dermatologists have an average accuracy of 62% to 80% in skin cancer diagnosis. The research community has been made significant progress in developing automated tools to assist dermatologists in decision making. In this work, we propose an automated computer-aided diagnosis system for multi-class skin (MCS) cancer classification with an exceptionally high accuracy. The proposed method outperformed both expert dermatologists and contemporary deep learning methods for MCS cancer classification. We performed fine-tuning over seven classes of HAM10000 dataset and conducted a comparative study to analyse the performance of five pre-trained convolutional neural networks (CNNs) and four ensemble models. The maximum accuracy of 93.20% for individual model amongst the set of models whereas maximum accuracy of 92.83% for ensemble model is reported in this paper. We propose use of ResNeXt101 for the MCS cancer classification owing to its optimized architecture and ability to gain higher accuracy. Keywords Skin Cancer . Dermoscopy . Classification . Deep convolutional neural network
* Jitendra V. Tembhurne [email protected] Saket S. Chaturvedi [email protected] Tausif Diwan [email protected] Extended author information available on the last page of the article
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1 Introduction The epidermis is the superficial layer of skin mainly consists of three cells: Squamous cells, Basal cells, and Melanocytes, as shown in Fig. 1. The outermost cells are Squamous and lowermost layer cells are Basal cells of the epidermis. Melanocytes protect deeper layers of skin from exposure of sun by producing melanin, a brown pigment substance [10]. When these cells experience excessive ultraviolet light exposure, the DNA mutations induced affects the growth of skin cells and eventually shapes in skin cancer [38, 57]. Squamous Cell Carcinoma, Basal Cell Carcinoma, and Melanoma are the substantial categories of skin cancer usually associated with squamous cells, basal cells, and melanocytes, respectively. The World Health Organization estimates skin cancer as one-third of all the diagnosed cancers cases globally [76]. Skin Cancer is a global public health issue which causes approximately 5.4 million newly identified skin cancer incidences in the United States each year [63]. However, melanomas are responsible for app
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