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Artificial Intelligence-Based Image Classification Techniques for Clinician Diagnosis of Skin Cancer

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Abstract

Skin cancer has become one of the common forms of malignancy in humans. The early and effective detection of these malignancies is essential for the effective treatment of the patients. However, accurate diagnosis of these lesions by visual examination of images is tedious, laborious, and fault-prone. Many computer-aided diagnosis methods have been devised for the diagnosis and detection of lesions, but these existing techniques perform poorly on challenging features of the lesions. The main aim of this work is to review different Artificial-intelligence (AI) dependent lesion classification techniques for the prognosis of skin cancer. Further, this study will help in determining the essential components required by a system for the diagnosis of skin lesions. For this purpose, secondary research was performed with the help of latest research articles. A literature review of collected papers was conducted to identify the technique used and the merits and demerits of each approach or study. On the basis of this review, system architecture with essential components has also been suggested. This study will provide a comprehensive insight into the already existing techniques for the categorisation of skin-related lesions.

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Author information

Authors and Affiliations

  1. Study Group Australia, Darlinghurst, Australia

    Shreyanth R. Chamakura

  2. Kent Institute Australia, Sydney, Australia

    P. W. C. Prasad, Hanspreet Kaur & Qurat Ul Ain Nizamani

  3. University of Duhok, Duhok, KRI, Iraq

    Ali Abas Albabawat & Razwan Mohmed Salah

Authors
  1. Shreyanth R. Chamakura

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  2. P. W. C. Prasad

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  3. Ali Abas Albabawat

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  4. Hanspreet Kaur

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  5. Qurat Ul Ain Nizamani

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  6. Razwan Mohmed Salah

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Corresponding author

Correspondence toAli Abas Albabawat.

Editor information

Editors and Affiliations

  1. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  2. Kent Institute Australia, Sydney, NSW, Australia

    Abeer Al Sadoon

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Chamakura, S.R., Prasad, P.W.C., Albabawat, A.A., Kaur, H., Nizamani, Q.U.A., Salah, R.M. (2022). Artificial Intelligence-Based Image Classification Techniques for Clinician Diagnosis of Skin Cancer. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the ICR’22 International Conference on Innovations in Computing Research. ICR 2022. Advances in Intelligent Systems and Computing, vol 1431. Springer, Cham. https://doi.org/10.1007/978-3-031-14054-9_11

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Chapter
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eBook
JPY 25167
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  • Available as EPUB and PDF
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Softcover Book
JPY 31459
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  • Compact, lightweight edition
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Purchases are for personal use only

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