COMPARATIVE ANALYSIS OF EFFICIENTNET AND RESNET MODELS IN THE CLASSIFICATION OF SKIN CANCER

Daniel Hartanto, Rosita Herawati

Abstract


Skin cancer get classified as one of the most common types of cancer cause to death. There are some types of skin cancer as: basal cell carcinoma (BCC), melanoma (MEL), and others. This cancer may have different symptoms depending on the type of skin cancer, but the most common signs include changes in the size, shape, or color of a mole or skin. The progress in machine learning has been increasing, mainly on deep learning and artificial intelegenct. In the recent past deep learning has been developed for medical research. In the latest papers, algorithms that have been applied for medical research are pre-trained models. In this research, the author compares the pre-trained EffecientNet and ResNet-50 for classification of skin cancer on the HAM10000 dataset to find out which is the best for classifying skin cancer and what is the best pre-trained model for skin cancer classification. This study aims to find the pre-trained EffecientNet and ResNet-50 models for accurate and efficient  for skin cancer classification. In this experiment the results obtained were: that the highest accuracy on test was achieved by EfficientNet B7 on 88.41% accuracy and the lowest accuracy on test was achieved by ResNet 50 on 83.42% accuracy.


Keywords


skin cancer; Pre-trained; EfficientNet; ResNet-50

Full Text:

PDF

References


H. Huang, B. W. Hsu, C. Lee, and V. S. Tseng, “Development of a light‐weight deep learning model for cloud applications and remote diagnosis of skin cancers,” J. Dermatol., vol. 48, no. 3, pp. 310–316, Mar. 2021, doi: 10.1111/1346-8138.15683.

K. Ali, Z. A. Shaikh, A. A. Khan, and A. A. Laghari, “Multiclass skin cancer classification using EfficientNets – a first step towards preventing skin cancer,” Neuroscience Informatics, vol. 2, no. 4, p. 100034, Dec. 2022, doi: 10.1016/j.neuri.2021.100034.

Md. K. Islam et al., “Melanoma Skin Lesions Classification using Deep Convolutional Neural Network with Transfer Learning,” in 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), Riyadh, Saudi Arabia: IEEE, Apr. 2021, pp. 48–53. doi: 10.1109/CAIDA51941.2021.9425117.

M. Tahir, A. Naeem, H. Malik, J. Tanveer, R. A. Naqvi, and S.-W. Lee, “DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images,” Cancers, vol. 15, no. 7, p. 2179, Apr. 2023, doi: 10.3390/cancers15072179.

M. S. Ali, M. S. Miah, J. Haque, M. M. Rahman, and M. K. Islam, “An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models,” Machine Learning with Applications, vol. 5, p. 100036, Sep. 2021, doi: 10.1016/j.mlwa.2021.100036.

D. Popescu, M. El-khatib, and L. Ichim, “Skin Lesion Classification Using Collective Intelligence of Multiple Neural Networks,” Sensors, vol. 22, no. 12, Art. no. 12, Jan. 2022, doi: 10.3390/s22124399.

A. Khamparia, P. K. Singh, P. Rani, D. Samanta, A. Khanna, and B. Bhushan, “An internet of health things-driven deep learning framework for detection and classification of skin cancer using transfer learning,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 7, p. e3963, 2021, doi: 10.1002/ett.3963.

S. Mohapatra, N. v. s. Abhishek, D. Bardhan, A. A. Ghosh, and S. Mohanty, “Comparison of MobileNet and ResNet CNN Architectures in the CNN-Based Skin Cancer Classifier Model,” in Machine Learning for Healthcare Applications, John Wiley & Sons, Ltd, 2021, pp. 169–186. doi: 10.1002/9781119792611.ch11.

N. Abuared, A. Panthakkan, M. Al-Saad, S. A. Amin, and W. Mansoor, “Skin Cancer Classification Model Based on VGG 19 and Transfer Learning,” in 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS), DUBAI, United Arab Emirates: IEEE, Nov. 2020, pp. 1–4. doi: 10.1109/ICSPIS51252.2020.9340143.

A. Tajerian, M. Kazemian, M. Tajerian, and A. A. Malayeri, “Design and validation of a new machine-learning-based diagnostic tool for the differentiation of dermatoscopic skin cancer images,” PLOS ONE, vol. 18, no. 4, p. e0284437, Apr. 2023, doi: 10.1371/journal.pone.0284437.

A. Rastogi, “ResNet50,” Medium. Accessed: Jun. 21, 2023. [Online]. Available: https://blog.devgenius.io/resnet50-6b42934db431




DOI: https://doi.org/10.24167/proxies.v7i2.12468

Copyright (c) 2024 Proxies : Jurnal Informatika



View My Stats