A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems

Arash Heidari; Danial Javaheri; Shiva Toumaj; Nima Jafari Navimipour; Mahsa Rezaei; Mehmet Unal

doi:10.1016/j.artmed.2023.102572

A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems

Artif Intell Med. 2023 Jul:141:102572. doi: 10.1016/j.artmed.2023.102572. Epub 2023 May 4.

Authors

Arash Heidari¹, Danial Javaheri², Shiva Toumaj³, Nima Jafari Navimipour⁴, Mahsa Rezaei⁵, Mehmet Unal⁶

Affiliations

¹ Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran.
² Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea.
³ Urmia University of Medical Sciences, Urmia, Iran.
⁴ Department of Computer Engineering, Kadir Has University, Istanbul, Turkiye; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan. Electronic address: nima.navimipour@khas.edu.tr.
⁵ Tabriz University of Medical Sciences, Faculty of Surgery, Tabriz, Iran.
⁶ Department of Computer Engineering, Nisantasi University, Istanbul, Turkiye.

PMID: 37295902
DOI: 10.1016/j.artmed.2023.102572

Abstract

With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.

Keywords: Blockchain; CapsNets; Chest CT; Deep Learning; Federated Learning; Lung cancer.

MeSH terms

Algorithms
Blockchain*
Data Science
Humans
Lung Neoplasms* / diagnostic imaging
Tomography, X-Ray Computed