AcneNet - A deep CNN based classification approach for acne classes

Masum Shah Junayed, Afsana Ahsan Jeny, Syeda Tanjila Atik, Nafis Neehal, Asif Karim, Sami Azam, Bharanidharan Shanmugam

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in ProceedingsResearchpeer-review

Abstract

Skin diseases are very common and nowadays easy to get remedy from. But, sometimes properly diagnosing these diseases can be quite troublesome due to the stiff hard-to-discriminate nature of the symptoms they exhibit. Deep Neural Networks, since its recent advent, has started outperforming different algorithms in almost every sectors. One of the problem domains, where Deep Neural Networks are really thriving today, is Image Classification and Object and Pattern Discovery from images. A special type of Deep Neural Network is Convolutional Neural Networks (CNN), which are being extensively used for different sorts of computer vision and image classification related problems. Hence, we have proposed a novel approach, where we have developed and used a Deep Residual Neural Network model for classifying five classes of Acnes from images. Our model has achieved an approximate accuracy as much as 99.44% for one class, and the rest were also above 94% with fairly high precision and recall score.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages203-208
Number of pages6
ISBN (Electronic)9781728121338
DOIs
Publication statusPublished - Jul 2019
Event12th International Conference on Information and Communication Technology and Systems, ICTS 2019 - Surabaya, Indonesia
Duration: 18 Jul 2019 → …

Publication series

NameProceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019

Conference

Conference12th International Conference on Information and Communication Technology and Systems, ICTS 2019
CountryIndonesia
CitySurabaya
Period18/07/19 → …

Fingerprint

Acne Vulgaris
Image classification
Neural Networks
Neural networks
Neural Networks (Computer)
Image Classification
Skin Diseases
Pattern Discovery
Computer vision
Skin
Neural Network Model
Computer Vision
Sort
Sector
Class
Deep neural networks
Model

Cite this

Junayed, M. S., Jeny, A. A., Atik, S. T., Neehal, N., Karim, A., Azam, S., & Shanmugam, B. (2019). AcneNet - A deep CNN based classification approach for acne classes. In Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019 (pp. 203-208). [8850935] (Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICTS.2019.8850935
Junayed, Masum Shah ; Jeny, Afsana Ahsan ; Atik, Syeda Tanjila ; Neehal, Nafis ; Karim, Asif ; Azam, Sami ; Shanmugam, Bharanidharan. / AcneNet - A deep CNN based classification approach for acne classes. Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019. IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 203-208 (Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019).
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title = "AcneNet - A deep CNN based classification approach for acne classes",
abstract = "Skin diseases are very common and nowadays easy to get remedy from. But, sometimes properly diagnosing these diseases can be quite troublesome due to the stiff hard-to-discriminate nature of the symptoms they exhibit. Deep Neural Networks, since its recent advent, has started outperforming different algorithms in almost every sectors. One of the problem domains, where Deep Neural Networks are really thriving today, is Image Classification and Object and Pattern Discovery from images. A special type of Deep Neural Network is Convolutional Neural Networks (CNN), which are being extensively used for different sorts of computer vision and image classification related problems. Hence, we have proposed a novel approach, where we have developed and used a Deep Residual Neural Network model for classifying five classes of Acnes from images. Our model has achieved an approximate accuracy as much as 99.44{\%} for one class, and the rest were also above 94{\%} with fairly high precision and recall score.",
keywords = "Acne diseases, Artificial Intelligence, CNN, Deep Residual Neural Network",
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Junayed, MS, Jeny, AA, Atik, ST, Neehal, N, Karim, A, Azam, S & Shanmugam, B 2019, AcneNet - A deep CNN based classification approach for acne classes. in Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019., 8850935, Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019, IEEE, Institute of Electrical and Electronics Engineers, pp. 203-208, 12th International Conference on Information and Communication Technology and Systems, ICTS 2019, Surabaya, Indonesia, 18/07/19. https://doi.org/10.1109/ICTS.2019.8850935

AcneNet - A deep CNN based classification approach for acne classes. / Junayed, Masum Shah; Jeny, Afsana Ahsan; Atik, Syeda Tanjila; Neehal, Nafis; Karim, Asif; Azam, Sami; Shanmugam, Bharanidharan.

Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019. IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 203-208 8850935 (Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019).

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in ProceedingsResearchpeer-review

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N2 - Skin diseases are very common and nowadays easy to get remedy from. But, sometimes properly diagnosing these diseases can be quite troublesome due to the stiff hard-to-discriminate nature of the symptoms they exhibit. Deep Neural Networks, since its recent advent, has started outperforming different algorithms in almost every sectors. One of the problem domains, where Deep Neural Networks are really thriving today, is Image Classification and Object and Pattern Discovery from images. A special type of Deep Neural Network is Convolutional Neural Networks (CNN), which are being extensively used for different sorts of computer vision and image classification related problems. Hence, we have proposed a novel approach, where we have developed and used a Deep Residual Neural Network model for classifying five classes of Acnes from images. Our model has achieved an approximate accuracy as much as 99.44% for one class, and the rest were also above 94% with fairly high precision and recall score.

AB - Skin diseases are very common and nowadays easy to get remedy from. But, sometimes properly diagnosing these diseases can be quite troublesome due to the stiff hard-to-discriminate nature of the symptoms they exhibit. Deep Neural Networks, since its recent advent, has started outperforming different algorithms in almost every sectors. One of the problem domains, where Deep Neural Networks are really thriving today, is Image Classification and Object and Pattern Discovery from images. A special type of Deep Neural Network is Convolutional Neural Networks (CNN), which are being extensively used for different sorts of computer vision and image classification related problems. Hence, we have proposed a novel approach, where we have developed and used a Deep Residual Neural Network model for classifying five classes of Acnes from images. Our model has achieved an approximate accuracy as much as 99.44% for one class, and the rest were also above 94% with fairly high precision and recall score.

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Junayed MS, Jeny AA, Atik ST, Neehal N, Karim A, Azam S et al. AcneNet - A deep CNN based classification approach for acne classes. In Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019. IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 203-208. 8850935. (Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019). https://doi.org/10.1109/ICTS.2019.8850935