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Detection of Malignant Melanoma Using Deep Learning

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Advances in Computing and Data Sciences (ICACDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1045))

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Abstract

Malignant melanoma is the most vicious and dangerous type of skin cancer, as it can easily diffuse to other regions of the body. This is the reason that mortality rates of melanoma are so immense. The deadly stats about melanoma can be overshadowed by the fact that melanoma can be cured if detected early. So, it is prominent to distinguish melanocytic and non-melanocytic lesions at initial stages. For this, use of Computer Aided Diagnosis systems are in vogue as they does not involve painful procedures and are effective in diagnosis. In this work, CAD system is developed based on deep learning concept, as in recent times this concept is becoming widely popular for yielding higher accuracies. Most popular deep neural network namely Convolutional neural networks are exploited. Two pretrained networks AlexNet and VGG16 have been used in two different ways. These ways are transfer learning and usage as feature extractor. It has been seen that transfer learning based concept yields efficient results for both CNNs, as AlexNet with transfer learning gives 95% of accuracy while AlexNet as a feature extractor provides accuracy of 90%. Same is observed with VGG16 as it shows accuracy of 97.5% for the former case and 95% for the latter case. Lastly, comparison of all techniques is carried out and it is evident that VGG16 with transfer learning outperform all by exhibiting accuracy, sensitivity and specificity of 97.5%, 100% and 96.87% respectively. Comparison of this method with other state-of-art methods has also been carried out and it is seen that our methodology outperform them. Apart from this, sensitivity achieved for both transfer learning based architectures is 100%, it means that all of the melanoma cases are diagnosed correctly.

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Authors and Affiliations

  1. Lovely Professional University, Phagwara, India

    Savy Gulati & Rosepreet Kaur Bhogal

Authors
  1. Savy Gulati
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  2. Rosepreet Kaur Bhogal
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Corresponding author

Correspondence to Savy Gulati .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Computer Science and Engineering, Jaypee Institute of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. ÚTIA AV ČR, Institute of Information Theory and Automation, Prague 8, Praha, Czech Republic

    Jan Flusser

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. CSE Department, Inderprastha Engineering College, Ghaziabad, Uttar Pradesh, India

    Rekha Kashyap

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Gulati, S., Bhogal, R.K. (2019). Detection of Malignant Melanoma Using Deep Learning. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-13-9939-8_28

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  • DOI: https://doi.org/10.1007/978-981-13-9939-8_28

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  • Print ISBN: 978-981-13-9938-1

  • Online ISBN: 978-981-13-9939-8

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