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Skin Lesion Prediction from Dermoscopic Images using Deep Learning

by Nazma Hossen Nishat, Pranta Paul, Farzina Akther, Tahmina Akter, Muhammad Anwarul Azim
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 18
Year of Publication: 2024
Authors: Nazma Hossen Nishat, Pranta Paul, Farzina Akther, Tahmina Akter, Muhammad Anwarul Azim
10.5120/ijca2024923577

Nazma Hossen Nishat, Pranta Paul, Farzina Akther, Tahmina Akter, Muhammad Anwarul Azim . Skin Lesion Prediction from Dermoscopic Images using Deep Learning. International Journal of Computer Applications. 186, 18 ( Apr 2024), 17-29. DOI=10.5120/ijca2024923577

@article{ 10.5120/ijca2024923577,
author = { Nazma Hossen Nishat, Pranta Paul, Farzina Akther, Tahmina Akter, Muhammad Anwarul Azim },
title = { Skin Lesion Prediction from Dermoscopic Images using Deep Learning },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2024 },
volume = { 186 },
number = { 18 },
month = { Apr },
year = { 2024 },
issn = { 0975-8887 },
pages = { 17-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number18/skin-lesion-prediction-from-dermoscopic-images-using-deep-learning/ },
doi = { 10.5120/ijca2024923577 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-04-27T03:06:59.262443+05:30
%A Nazma Hossen Nishat
%A Pranta Paul
%A Farzina Akther
%A Tahmina Akter
%A Muhammad Anwarul Azim
%T Skin Lesion Prediction from Dermoscopic Images using Deep Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 18
%P 17-29
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Skin lesions, which comprise a wide range of irregularities in skin appearance, might serve as precursors of skin cancer due to the complex interaction of hereditary variables and longterm UV ex- posure. Significant advances in dermatology have been made with the use of deep learning models, notably convolutional neural net- works (CNNs). These models excel in analyzing dermatoscopic pictures, allowing for early and accurate identification of a vari- ety of skin problems. In this work, a complete evaluation of deep learning models for predicting skin lesions is conducted, with an emphasis on accuracy. Notable performers include DenseNet169 and ResNet101, both of which achieve an outstanding 91% accu- racy. Furthermore, a hybrid model obtains an accuracy of 89%, in- dicating its capacity to recognize complicated visual patterns. The study investigates model fusion strategies to capitalize on possible synergy in prediction skills, ultimately improving automated der- matological diagnosis systems. Notable models are DenseNet121, ResNet-50V2, and InceptionResNetV2, which contribute consider- ably with accuracies of 91%, 89%, and 85%, respectively, while MobileNetV2 and VGG-16 provide accuracies of 82% and 80%. These advances, taken together, enable the development of strong and accurate diagnostic technologies capable of efficiently expedit- ing skin health interventions.

References
  1. Neeshma, A. and Nair, C.S., 2022, August. Multiclass Skin Lesion Classification Using Densenet. In 2022 Third Interna- tional Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) (pp. 506-510). IEEE.
  2. SO¨ NMEZ, A.F., C¸ AKAR, S., CEREZC˙I, F., KOTAN,
  3. M., DEL˙IBAS¸ OG˘ LU, ˙I. and Gu¨lu¨zar, C¸ .˙I.T., 2023. Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions. Sakarya University Journal of Computer and Information Sciences, 6(2), pp.114-122.
  4. Jasil, S.G. and Ulagamuthalvi, V., 2021, May. Skin lesion classification using pre-trained DenseNet201 deep neural net- work. In 2021 3rd international conference on signal process- ing and communication (ICPSC) (pp. 393-396). IEEE.
  5. Wu, Y., Lariba, A.C., Chen, H. and Zhao, H., 2022, July. Skin Lesion Classification based on Deep Convolutional Neu- ral Network. In 2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems (ICPICS) (pp. 376-380). IEEE.
  6. Purnama, I.K.E., Hernanda, A.K., Ratna, A.A.P., Nurtanio, I., Hidayati, A.N., Purnomo, M.H., Nugroho, S.M.S. and Rach- madi, R.F., 2019, November. Disease classification based on dermoscopic skin images using convolutional neural network in teledermatology system. In 2019 international conference on computer engineering, network, and intelligent multimedia (CENIM) (pp. 1-5). IEEE.
  7. Salian, A.C., Vaze, S., Singh, P., Shaikh, G.N., Chapaneri, S. and Jayaswal, D., 2020, April. Skin lesion classification using deep learning architectures. In 2020 3rd International confer- ence on communication system, computing and IT applica- tions (CSCITA) (pp. 168-173). IEEE.
  8. Quang, N.H., 2017, November. Automatic skin lesion anal- ysis towards melanoma detection. In 2017 21st Asia Pacific symposium on intelligent and evolutionary systems (IES) (pp. 106-111). IEEE.
  9. Mikołajczyk, A. and Grochowski, M., 2018, May. Data aug- mentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD work- shop (IIPhDW) (pp. 117-122). IEEE.
  10. “Skin Cancer MNIST: HAM10000,” www.kaggle.com. https://www.kaggle.com/datasets/kmader/skin-cancer-mnist- ham10000
  11. Shetty, B., Fernandes, R., Rodrigues, A.P., Chengoden, R., Bhattacharya, S. and Lakshmanna, K., 2022. Skin lesion classification of dermoscopic images using machine learning and convolutional neural network. Scientific Reports, 12(1), p.18134.
  12. Mahbod, A., Schaefer, G., Wang, C., Ecker, R., Dorffner, G. and Ellinger, I., 2021, January. Investigating and exploiting image resolution for transfer learning-based skin lesion clas- sification. In 2020 25th international conference on pattern recognition (ICPR) (pp. 4047-4053). IEEE.
  13. Zhang, Y. and Wang, C., 2021, March. SIIM-ISIC melanoma classification with DenseNet. In 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE) (pp. 14-17). IEEE.
  14. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. and Chen, L.C., 2018. Mobilenetv2: Inverted residuals and linear bottle- necks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520).
  15. Ogundokun, R.O., Li, A., Babatunde, R.S., Umezuruike, C., Sadiku, P.O., Abdulahi, A.T. and Babatunde, A.N., 2023. En- hancing skin cancer detection and classification in dermo- scopic images through concatenated MobileNetV2 and xcep- tion models. Bioengineering, 10(8), p.979.
  16. Arya, M.S., Prabahavathy, P. and Ahamed, S., 2022. Skin le- sion classification and Prediction by Data Augmentation in HAM10000 and ISIC 2019 dataset.
  17. Kondaveeti, H.K. and Edupuganti, P., 2020, December. Skin cancer classification using transfer learning. In 2020 IEEE In- ternational Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI) (pp. 1-4). IEEE.
  18. Shah, V., Autee, P. and Sonawane, P., 2020, December. Detec- tion of melanoma from skin lesion images using deep learning techniques. In 2020 International Conference on Data Science and Engineering (ICDSE) (pp. 1-8). IEEE.
  19. Quang, N.H., 2017, November. Automatic skin lesion anal- ysis towards melanoma detection. In 2017 21st Asia Pacific symposium on intelligent and evolutionary systems (IES) (pp. 106-111). IEEE.
  20. Salian, A.C., Vaze, S., Singh, P., Shaikh, G.N., Chapaneri, S. and Jayaswal, D., 2020, April. Skin lesion classification using deep learning architectures. In 2020 3rd International confer- ence on communication system, computing and IT applica- tions (CSCITA) (pp. 168-173). IEEE.
  21. Ahmed, S.A.A., Yanikog˘lu, B., Go¨ksu, O¨ . and Aptoula, E., 2020, October. Skin lesion classification with deep CNN en- sembles. In 2020 28th Signal Processing and Communica- tions Applications Conference (SIU) (pp. 1-4). IEEE.
  22. Nguyen, V.D., Bui, N.D. and Do, H.K., 2022. Skin lesion classification on imbalanced data using deep learning with soft attention. Sensors, 22(19), p.7530.
  23. Sachdev, J., Shekhar, S. and Indu, S., 2018, April. Melanoma screening using deep neural networks. In 2018 3rd Interna- tional Conference for Convergence in Technology (I2CT) (pp. 1-5). IEEE.
  24. Mahbod, A., Schaefer, G., Wang, C., Ecker, R. and Ellinge, I., 2019, May. Skin lesion classification using hybrid deep neu- ral networks. In ICASSP 2019-2019 IEEE international con- ference on acoustics, speech and signal processing (ICASSP) (pp. 1229-1233). IEEE.
  25. Lopez, A.R., Giro-i-Nieto, X., Burdick, J. and Marques, O., 2017, February. Skin lesion classification from dermo- scopic images using deep learning techniques. In 2017 13th IASTED international conference on biomedical engineering (BioMed) (pp. 49-54). IEEE.
  26. Moldovan, D., 2019, November. Transfer learning based method for two-step skin cancer images classification. In 2019 E-Health and Bioengineering Conference (EHB) (pp. 1- 4). IEEE.
  27. Jain, S., Singhania, U., Tripathy, B., Nasr, E.A., Aboudaif, M.K. and Kamrani, A.K., 2021. Deep learning-based trans- fer learning for classification of skin cancer. Sensors, 21(23), p.8142
Index Terms

Computer Science
Information Sciences

Keywords

CNN Transfer Learning Data Balancing Augmentation Hybird Model