TY - GEN
T1 - AcneNet - A deep CNN based classification approach for acne classes
AU - Junayed, Masum Shah
AU - Jeny, Afsana Ahsan
AU - Atik, Syeda Tanjila
AU - Neehal, Nafis
AU - Karim, Asif
AU - Azam, Sami
AU - Shanmugam, Bharanidharan
PY - 2019/7
Y1 - 2019/7
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.
KW - Acne diseases
KW - Artificial Intelligence
KW - CNN
KW - Deep Residual Neural Network
UR - http://www.scopus.com/inward/record.url?scp=85073513639&partnerID=8YFLogxK
U2 - 10.1109/ICTS.2019.8850935
DO - 10.1109/ICTS.2019.8850935
M3 - Conference Paper published in Proceedings
T3 - Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019
SP - 203
EP - 208
BT - Proceedings of 2019 International Conference on Information and Communication Technology and Systems, ICTS 2019
PB - IEEE, Institute of Electrical and Electronics Engineers
CY - Piscataway, NJ
T2 - 12th International Conference on Information and Communication Technology and Systems, ICTS 2019
Y2 - 18 July 2019 through 18 July 2019
ER -