Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

By R. Kumar, A. Khan, Sinmin Zhang, Jay Kumar, Ting Yang, Noorbakhash Amiri Golalirz, Zakria, Ikram Ali, Sidra Shafiq, Wenyong Wang. IEEE Sensors Journal, 2020

401 citations recorded by the source corpus.

With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients’ data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/238878e4b4d7ed4a830be1598e610b2a9bec563a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Qiang Yang
2. Bingsheng He
3. Qinbin Li
4. Mingxia Liu
5. Lawrence Carin
6. Ming Ding
7. Dawn Song
8. Jianyi Zhang

In Health Informatics

1. Bingsheng He
2. Qiang Yang
3. Qinbin Li

In Radiology, Nuclear Medicine and Imaging

1. Dawn Song
2. Bingsheng He
3. Lawrence Carin
4. Quoc-Viet Pham
5. Qiang Yang
6. Mingxia Liu
7. Qinbin Li

Citations in the corpus

• Federated Learning: Opportunities and Challenges (2021, methodology): “Works such as [4, 7, 19, 27] propose Federated Learning in a block-chain framework.”
• Federated Learning for Smart Healthcare: A Survey (2021, methodology): “For example, a number of hospitals can cooperatively communicate via the blockchain to run CNN updates locally for identifying CT scans of COVID- 19 patients [112].”
• Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, neutral): “Then, given the specified states, the transition probability matrix of states R ( m ) , m = 1 , was obtained per each indicator as follows: The tran siti on prob abil ity matr ix of stat es for the numb er of disc harg ed cases”
• FLOP: Federated Learning on Medical Datasets using Partial Networks (2021, background): “Recent works [19, 21] focusing on federated learning for COVID-19 typically rely on sharing the full model between clients.”
• Federated learning for medical image analysis: A survey (2023, background): “Some work has made efforts on this point for medical image analysis through leveraging blockchain [178], [179] or other decentralization methods [180].”
• Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “Very recently, the potential of FL to support the fighting against infectious diseases such as COVID-19 [127] has been investigated [128], as indicated in Fig.”
• Model-Contrastive Federated Learning (2021, methodology): “Moreover, it has been applied in many applications such as medical imaging [21, 23], object detection [32], and landmark classification [15].”