Lung Disease Diagnosis based on Transfer Learning
DOI: 10.23977/jaip.2022.050113 | Downloads: 24 | Views: 693
Author(s)
Wanle Chi 1,2, Yun Huoy Choo 2, Ong Sing Goh 2, Gong Dafeng 1,2
Affiliation(s)
1 College of artificial intelligence, Wenzhou Polytechnic, Wenzhou, Zhejiang 325035, China
2 Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Malacca 76100, Malaysia
Corresponding Author
Wanle ChiABSTRACT
The diagnosis of lung nodules is an important indicator of clinical indications of malignant lung diseases such as lung cancer. Traditionally, doctors read CT lung nodule image to judge the lung disease. The difficulty of doctors' judgement leads to missed diagnosis and misdiagnosis. Through the cooperation of human intelligence and artificial intelligence, computer aided diagnosis can improve the medical imaging clinical diagnosis, improve diagnosis efficiency and accuracy.The objective of paper is to develop a computer-aided diagnostic predictor for lung disease. This paper proposes using cheap and many malignancy labels to transfer learn to expensive and few pathological diagnosis. The paper proposes a partially soft parameter sharing method. In the LIDC datasets, the result of experiment shows that the algorithm of paper is more accurate than other approaches.
KEYWORDS
Lung Disease Diagnosis, LIDC; Lung Image, Transfer Learning, Multi-Tasking Learning, Soft Parameter SharingCITE THIS PAPER
Wanle Chi, Yun Huoy Choo, Ong Sing Goh and Gong Dafeng, Lung Disease Diagnosis based on Transfer Learning. Journal of Artificial Intelligence Practice (2022) Vol. 5: 98-104. DOI: http://dx.doi.org/10.23977/jaip.2022.050113.
REFERENCES
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