An Integration of blockchain and AI for secure data sharing and detection of CT images for the hospitals. (January 2021)

• Record Type: Journal Article
• Authors: Kumar, Rajesh; Wang, WenYong; Kumar, Jay; Yang, Ting; Khan, Abdullah; Ali, Wazir; Ali, Ikram
• Is Part Of: Computerized medical imaging and graphics. Volume 87(2021)
• Publication Date: 2021-01
• Subjects: Blockchain -- CT SCAN imaging -- Deep learning -- Lung cancer Detection -- Secure data sharing; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2020.101812
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: We propose a novel framework that combines deep learning with blockchain to provide learning over decentralized data sources. We design a customized smart contract to establish a secure large-scale real-time data sharing among different data providers. We modify the RCNN by integrating the Region of Interest (ROI) pooling layer to detect the region of interest and train in a decentralized manner network. Finally, an intensive empirical study is conducted to validate our proposed method through the blockchain and deep neural network. Abstract: Deep learning, for image data processing, has been widely used to solve a variety of problems related to medical practices. However, researchers are constantly struggling to introduce ever efficient classification models. Recent studies show that deep learning can perform better and generalize well when trained using a large amount of data. Organizations such as hospitals, testing labs, research centers, etc. can share their data and collaboratively build a better learning model. Every organization wants to retain the privacy of their data, while on the other hand, these organizations want accurate and efficient learning models for various applications. The concern for privacy in medical data limits the sharing of data among multiple organizations due to some ethical and legal issues. To retain privacy and enable data sharing, we present a unique method that combines locally learned deep learning models over the blockchain to improve the prediction of lung cancer in health-care systems by filling the defined gap. There are several challenges involved in sharing that data while maintaining privacy. In this paper, we identify and address such challenges. The contribution of our work is four-fold: (i) We propose a method to secure medical data by only sharing the weights of the trained deep learning model via smart contract. (ii) To deal with different sized computed tomography (CT) images from various sources, we adopted the Bat algorithm and data augmentation to reduce the noise and overfitting for the global learning model. (iii) We distribute the local deep learning model wights to the blockchain decentralized network to train a global model. iv) We propose a recurrent convolutional neural network (RCNN) to estimate the region of interest (ROI) in theCT images. An extensive empirical study has been conducted to verify the significance of our proposed method for better prediction of cancer in the early stage. Experimental results of the proposed model can show that our proposed technique can detect the lung cancer nodules and also achieve better performance.