Abstract
Machine learning techniques have provided a technological evolution in medicine and especially in the field of medical imaging. The aim of this study is to firstly compare multiple transfer learning architecture models such as MobilleNetVn (n = 1, 2), InceptionVn (n = 1, 2, 3, 4), InceptionResNet, VGG16 and NasNetMobile and provide a final performance estimation, using a variety of metrics, secondly, propose an efficient and accurate classifier for liver cancer trait detection and prediction, and thirdly, develop a mobile application which uses the proposed model to classify liver cancer traits into various categories in real-time. Magnetic Resonance Images (MRI) of mouse liver cancer of different origin were used as input datasets for our experiments. However, the required memory by the deployed Convolutional Neural Network (CNN) models on smart mobile devices or embedded systems for real time applications, is an issue to be addressed. Here, all the baseline pre-trained CNN models on the ImageNet dataset were trained on a dataset of MRI images of mice of different genetic background with genetically- or chemically- induced hepatocellular cancer. We present and compare all main metric values for each model such as accuracy, cross entropy, f-score, confusion matrix for various types of classification. Data analysis verifies that the proposed optimized architecture model for this task of liver cancer trait classification and prediction, the MV1-LCCP, shows a suitable performance in terms of memory utilization and accuracy, suitable to be deployed in the mobile Android application, which is also developed and presented in this paper.
O. Giannou and A.D. Giannou—Equal co-first authors.
S. Huber and G. Pavlidis—Equal co-senior authors.
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Acknowledgement
The authors thank the in vivo MRI Core Facility at the University Medical Center Hamburg-Eppendorf for their technical assistance. Funding: This work was supported in part by the Deutsche Forschungsgemeinschaft (SFB841 to S.H. and A.D.G.), the Ernst Jung-Stiftung Hamburg (to S.H.), the European Respiratory Society/short term fellowship (to A.D.G.), the Else Kröner Memorial Stipendium (to A.D.G.), the Werner Otto Stiftung (to A.D.G.), the Erich und Gertrud Roggenbuck Stiftung (to A.D.G.), the Hamburger Krebsgesellschaft Stiftung (to A.D.G.). S.H. has an endowed Heisenberg-Professorship awarded by the Deutsche Forschungsgemeinschaft.
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Giannou, O. et al. (2021). Liver Cancer Trait Detection and Classification Through Machine Learning on Smart Mobile Devices. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_8
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