Nugroho, Hanung Adi and Frannita, Eka Legya (2021) Thyroid Cancer Classification using Transfer Learning. In: 2021 International Conference on Computer Science and Engineering (IC2SE), 16 November 2021, Padang.
Full text not available from this repository. (Request a copy)Abstract
The growth of artificial intelligence has been successfully reached remarkable findings in all fields including of medical cases. The powerful performance of artificial intelligence successfully became a second opinion to assist the medical personnel in making diagnosis decision. In the thyroid cancer cases, the growth of artificial intelligence offered valuable benefit since thyroid examination procedures highly depended on the medical personnel skills and experiences. One of the popular findings was that the implementation of transfer learning to find the best network for the targeted problem. In this study, we conducted experiment to develop a classification method for classifying thyroid cancer using transfer learning method. Our experiment was performed in the thyroid public dataset consisting of 348 thyroid ultrasound images divided into two classes (benign and malignant). In this study, we performed DenseNet121 and NasNetLarge as the best networks according to previous similar studies. According to our experiment, NasNetLarge obtained better accuracy than DenseNet121 by 8 of improvement. This result indicated that NasNetLarge was a powerful CNN for classifying the thyroid cancer. © 2021 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Cited by: 0 |
Uncontrolled Keywords: | Deep learning; Diagnosis; Diseases; Learning systems; Personnel; Transfer learning; Ultrasonic applications; Cancer classification; Cancer detection; Deep learning; Medical case; Medical personnel; Performance; Second opinions; Thyroid cancers; Transfer learning; Ultrasound images; Image classification |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Electronics Engineering Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 28 Oct 2024 04:26 |
Last Modified: | 28 Oct 2024 04:26 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8545 |