TY - JOUR
T1 - Secret learning for lung cancer diagnosis—a study with homomorphic encryption, texture analysis and deep learning
AU - Adhikary, Subhrangshu
AU - Dutta, Subhayu
AU - Dwivedi, Ashutosh Dhar
N1 - © 2023 © 2022 IOP Publishing Ltd.
PY - 2024/1
Y1 - 2024/1
N2 - Advanced lung cancer diagnoses from radiographic images include automated detection of lung cancer from CT-Scan images of the lungs. Deep learning is a popular method for decision making which can be used to classify cancerous and non-cancerous lungs from CT-Scan images. There are many experiments which show the uses of deep learning for performing such classifications but very few of them have preserved the privacy of users. Among existing methods, federated learning limits data sharing to a central server and differential privacy although increases anonymity the original data is still shared. Homomorphic encryption can resolve the limitations of both of these. Homomorphic encryption is a cryptographic technique that allows computations to be performed on encrypted data. In our experiment, we have proposed a series of textural information extraction with the implementation of homomorphic encryption of the CT-Scan images of normal, adenocarcinoma, large cell carcinoma and squamous cell carcinoma. We have further processed the encrypted data to make it classifiable and later we have classified it with deep learning. The results from the experiments have obtained a classification accuracy of 0.9347.
AB - Advanced lung cancer diagnoses from radiographic images include automated detection of lung cancer from CT-Scan images of the lungs. Deep learning is a popular method for decision making which can be used to classify cancerous and non-cancerous lungs from CT-Scan images. There are many experiments which show the uses of deep learning for performing such classifications but very few of them have preserved the privacy of users. Among existing methods, federated learning limits data sharing to a central server and differential privacy although increases anonymity the original data is still shared. Homomorphic encryption can resolve the limitations of both of these. Homomorphic encryption is a cryptographic technique that allows computations to be performed on encrypted data. In our experiment, we have proposed a series of textural information extraction with the implementation of homomorphic encryption of the CT-Scan images of normal, adenocarcinoma, large cell carcinoma and squamous cell carcinoma. We have further processed the encrypted data to make it classifiable and later we have classified it with deep learning. The results from the experiments have obtained a classification accuracy of 0.9347.
KW - convolution neural network
KW - CT scan
KW - deep learning
KW - homomorphic encryption
KW - lung cancer
UR - http://www.scopus.com/inward/record.url?scp=85180004562&partnerID=8YFLogxK
U2 - 10.1088/2057-1976/ad0b4b
DO - 10.1088/2057-1976/ad0b4b
M3 - Journal article
C2 - 37944251
AN - SCOPUS:85180004562
SN - 2057-1976
VL - 10
JO - Biomedical Physics and Engineering Express
JF - Biomedical Physics and Engineering Express
IS - 1
M1 - 015011
ER -