Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments

Nipun Shantha Kahatapitiya; Akila Wijethunge; Sajith Edirisinghe; Bhagya Nathali Silva; Mansik Jeon; Jeehyun Kim; Udaya Wijenayake; Ruchire Eranga Wijesinghe

doi:10.1109/ICAC60630.2023.10417424

Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments

Nipun Shantha Kahatapitiya
, Akila Wijethunge
, Sajith Edirisinghe
, Bhagya Nathali Silva
, Mansik Jeon
, Jeehyun Kim
, Udaya Wijenayake
, Ruchire Eranga Wijesinghe

University of Sri Jayewardenepura
Sri Lanka Institute of Information Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Early detection of skin conditions is crucial, and some skin conditions can become more difficult to treat if left untreated. The gold standard Dermatoscope is a non-invasive technique used for the examination and evaluation of skin lesions, which is equipped with a magnifying lens and a light source. However, precise inspection of existing dermatoscopes has become a limitation due to the unavailability of image-analyzing methods. Herein, this study reports the successful development of a Convolutional Neural Networks (CNN) based, Artificial intelligence (AI)-Dermatoscope integrating optics and a smart illumination system to enhance the accurate examination of acne conditions of the skin. The system was trained on a large dataset of acne to accurately identify and classify skin conditions. Finally, the system utilizes CNN knowledge to predict new images of skin and provide diagnostic information to doctors and other healthcare professionals. Thus, this system will improve the accuracy and speed of skin diagnosis, and consequently, improve the health-related quality of life of patients.

Original language	English
Title of host publication	ICAC 2023 - 5th International Conference on Advancements in Computing
Subtitle of host publication	Technological Innovation for a Sustainable Economy, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	852-856
Number of pages	5
ISBN (Electronic)	9798350358131
DOIs	https://doi.org/10.1109/ICAC60630.2023.10417424
State	Published - 2023
Event	5th International Conference on Advancements in Computing, ICAC 2023 - Colombo, Sri Lanka Duration: 7 Dec 2023 → 8 Dec 2023

Publication series

Name	ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings

Conference

Conference	5th International Conference on Advancements in Computing, ICAC 2023
Country/Territory	Sri Lanka
City	Colombo
Period	7/12/23 → 8/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

acne
computer vision
convolutional neural network (CNN)
dermatoscope
image processing
optics
skin conditions

Access to Document

10.1109/ICAC60630.2023.10417424

Cite this

Kahatapitiya, N. S., Wijethunge, A., Edirisinghe, S., Silva, B. N., Jeon, M., Kim, J., Wijenayake, U., & Wijesinghe, R. E. (2023). Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments. In ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings (pp. 852-856). (ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAC60630.2023.10417424

Kahatapitiya, Nipun Shantha ; Wijethunge, Akila ; Edirisinghe, Sajith et al. / Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments. ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 852-856 (ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings).

@inproceedings{612d53eed242425487b78d34aaa04dcb,

title = "Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments",

abstract = "Early detection of skin conditions is crucial, and some skin conditions can become more difficult to treat if left untreated. The gold standard Dermatoscope is a non-invasive technique used for the examination and evaluation of skin lesions, which is equipped with a magnifying lens and a light source. However, precise inspection of existing dermatoscopes has become a limitation due to the unavailability of image-analyzing methods. Herein, this study reports the successful development of a Convolutional Neural Networks (CNN) based, Artificial intelligence (AI)-Dermatoscope integrating optics and a smart illumination system to enhance the accurate examination of acne conditions of the skin. The system was trained on a large dataset of acne to accurately identify and classify skin conditions. Finally, the system utilizes CNN knowledge to predict new images of skin and provide diagnostic information to doctors and other healthcare professionals. Thus, this system will improve the accuracy and speed of skin diagnosis, and consequently, improve the health-related quality of life of patients.",

keywords = "acne, computer vision, convolutional neural network (CNN), dermatoscope, image processing, optics, skin conditions",

author = "Kahatapitiya, \{Nipun Shantha\} and Akila Wijethunge and Sajith Edirisinghe and Silva, \{Bhagya Nathali\} and Mansik Jeon and Jeehyun Kim and Udaya Wijenayake and Wijesinghe, \{Ruchire Eranga\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th International Conference on Advancements in Computing, ICAC 2023 ; Conference date: 07-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/ICAC60630.2023.10417424",

language = "English",

series = "ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "852--856",

booktitle = "ICAC 2023 - 5th International Conference on Advancements in Computing",

address = "United States",

}

Kahatapitiya, NS, Wijethunge, A, Edirisinghe, S, Silva, BN, Jeon, M , Kim, J, Wijenayake, U & Wijesinghe, RE 2023, Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments. in ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings. ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 852-856, 5th International Conference on Advancements in Computing, ICAC 2023, Colombo, Sri Lanka, 7/12/23. https://doi.org/10.1109/ICAC60630.2023.10417424

Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments. / Kahatapitiya, Nipun Shantha; Wijethunge, Akila; Edirisinghe, Sajith et al.
ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 852-856 (ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments

AU - Kahatapitiya, Nipun Shantha

AU - Wijethunge, Akila

AU - Edirisinghe, Sajith

AU - Silva, Bhagya Nathali

AU - Jeon, Mansik

AU - Kim, Jeehyun

AU - Wijenayake, Udaya

AU - Wijesinghe, Ruchire Eranga

PY - 2023

Y1 - 2023

N2 - Early detection of skin conditions is crucial, and some skin conditions can become more difficult to treat if left untreated. The gold standard Dermatoscope is a non-invasive technique used for the examination and evaluation of skin lesions, which is equipped with a magnifying lens and a light source. However, precise inspection of existing dermatoscopes has become a limitation due to the unavailability of image-analyzing methods. Herein, this study reports the successful development of a Convolutional Neural Networks (CNN) based, Artificial intelligence (AI)-Dermatoscope integrating optics and a smart illumination system to enhance the accurate examination of acne conditions of the skin. The system was trained on a large dataset of acne to accurately identify and classify skin conditions. Finally, the system utilizes CNN knowledge to predict new images of skin and provide diagnostic information to doctors and other healthcare professionals. Thus, this system will improve the accuracy and speed of skin diagnosis, and consequently, improve the health-related quality of life of patients.

AB - Early detection of skin conditions is crucial, and some skin conditions can become more difficult to treat if left untreated. The gold standard Dermatoscope is a non-invasive technique used for the examination and evaluation of skin lesions, which is equipped with a magnifying lens and a light source. However, precise inspection of existing dermatoscopes has become a limitation due to the unavailability of image-analyzing methods. Herein, this study reports the successful development of a Convolutional Neural Networks (CNN) based, Artificial intelligence (AI)-Dermatoscope integrating optics and a smart illumination system to enhance the accurate examination of acne conditions of the skin. The system was trained on a large dataset of acne to accurately identify and classify skin conditions. Finally, the system utilizes CNN knowledge to predict new images of skin and provide diagnostic information to doctors and other healthcare professionals. Thus, this system will improve the accuracy and speed of skin diagnosis, and consequently, improve the health-related quality of life of patients.

KW - acne

KW - computer vision

KW - convolutional neural network (CNN)

KW - dermatoscope

KW - image processing

KW - optics

KW - skin conditions

UR - https://www.scopus.com/pages/publications/85186139950

U2 - 10.1109/ICAC60630.2023.10417424

DO - 10.1109/ICAC60630.2023.10417424

M3 - Conference contribution

AN - SCOPUS:85186139950

T3 - ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings

SP - 852

EP - 856

BT - ICAC 2023 - 5th International Conference on Advancements in Computing

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Advancements in Computing, ICAC 2023

Y2 - 7 December 2023 through 8 December 2023

ER -

Kahatapitiya NS, Wijethunge A, Edirisinghe S, Silva BN, Jeon M , Kim J et al. Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments. In ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 852-856. (ICAC 2023 - 5th International Conference on Advancements in Computing: Technological Innovation for a Sustainable Economy, Proceedings). doi: 10.1109/ICAC60630.2023.10417424

Development of Convolutional Neural Network-Based AI-Dermatoscope for Non-Invasive Skin Assessments

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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