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Role of Federated Learning Disease Prediction using Machine Learning 13
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RESULTS AND DISCUSSION
In this section, we present the results of the federated learning experiments on a
test dataset. The results are generated on a local device i.e. client-side and on the
aggregated server model. The procedure is demonstrated in Fig. (2). The models
trained on the client devices are aggregated and the global server model is
updated. Here, we use the “FedAvg” algorithm to aggregate the local models by
averaging their model parameters.
Categorical cross-entropy is used to evaluate the loss of the models. Categorical
cross-entropy is suited for multi class classification as one image can belong to
one category with a probability of 1, while other categories are given a probability
of 0.
Fig. (2). Procedure followed for updating the server model.
In the table above, each model is subdivided into- client 1, client 2, client 3, and
server. Here, each client is the local hospital/organization and the server is the
final global aggregated server. First, each client is given the deep learning model
which is trained on their individual dataset. The clients are evaluated locally and
then they communicate the model parameters to the server. Each client sends its
model parameters to the server, here we have 3 models, so there will be 3 model
parameters that will be aggregated by the FedAvg algorithm on the server.
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In VGG-16, client 1, client 2, and client 3 reported accuracies of 89.3%, 84.7%
and 90%, respectively. The final server model reported an accuracy of 95%, since
the models are averaged, an increase in accuracy can be seen. AlexNet’s server
model gave an accuracy of 87% while the client’s performed an average of 80%,
thus VGG-16 outperformed Alexnet. ResNet-101 and DenseNet-121 are both
very deep neural networks having 100+ layers and their reported accuracies are
very similar. The server model reported an accuracy of 97% and 98% for
resnet101 and densenet121 respectively. The clients when trained on resnet101
reported an accuracy of 92%, 83%, and 83% respectively. Here, client 1 trained
very effectively thus increasing the server accuracy after aggregation. A similar
situation is seen for DenseNet121, where the clients gave an accuracy of 92%,
85%, and 86%, respectively while the server accuracy increased substantially after
aggregation reporting an accuracy of 98%. The overall experiment highlights that
deeper neural networks performed quite well and DenseNet121 reported the
highest overall performance.
CONCLUSION
Federated learning is a technique in which the models train locally on the device
and the trained model parameters are communicated with the central server where
aggregation takes place. Federated learning tackles the problem of data privacy by
just sending the trained model parameters. In this research, we applied Federated
learning to train deep learning models to classify chest X-rays into Covid,
Pneumonia and Normal. The research assumes 3 data centers/hospitals that train
the models locally. Four deep learning models – AlexNet, VGG-16, DenseNet121
and ResNet101 are used to provide a comparative analysis.
Each client in the experiment was provided with a local training dataset. The
experiments showed that DenseNet121 recorded the highest accuracy of 98% on
the server model followed by ResNet101 which reported an accuracy of 97%.
VGG-16 and AlexNet reported global model accuracies of 95% and 88%
respectively. The clients keep on training their respective local models as new
data is generated. Thus federated learning models evolve and improve with time.
Federated learning has a lot of potential applications especially in the field of
Healthcare. In healthcare, it is very important that the privacy of the patient is
preserved and healthcare has de-centralized sources of data i.e. multiple hospitals
isolated from one another. This research provides a comparative study of deep
learning models in a federated setting. Further, this research can be applied to
different image classification problems in healthcare, and also the comparative
study presented gives insight into which deep learning models are best suited for
the task.
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REFERENCES
[1] J. Luo, X. Wu, Y. Luo, A. Huang, Y. Huang, and Y. Liu, "Real-world image datasets for federated
learning", arXiv, 2019p. 1910.11089.
[2] M. Agarwal, G. Rani, and V.S. Dhaka, "Optimized contrast enhancement for tumor detection", Int. J.
Imaging Syst. Technol., vol. 30, no. 3, pp. 687-703, 2020.
[http://dx.doi.org/10.1002/ima.22408]
[3] VS Dhaka, G Rani, MG Oza, T Sharma, and A Misra, "A deep learning model for mass screening of
COVID-19", Int J Imaging Syst Technol, vol. 31, no. 2, pp. 483-498, 2021.
[http://dx.doi.org/10.1002/ima.22544]
[4] G. Rani, M.G. Oza, V.S. Dhaka, N. Pradhan, S. Verma, and J.J.P.C. Rodrigues, "Applying deep
learning-based multi-modal for detection of coronavirus", Multimed. Syst., pp. 1-12, 2021.
[5] N. Kundu, G. Rani, and V.S. Dhaka, "Machine learning and IoT based disease predictor and alert
generator system", 2020 Fourth International Conference on Computing Methodologies and
Communication (ICCMC), 2020.
[http://dx.doi.org/10.1109/ICCMC48092.2020.ICCMC-000142]
[6] S.G. Savita, G. Rani, and D. Singh, "Machine learning and IoT based disease predictor and alert
generator system", In: 2020 Fourth International Conference on Computing Methodologies and
Communication (ICCMC) IEEE, 2020.
[7] S.G. Savita, G. Rania, and VS. Dhaka, "Efficient predictive modelling for classification of coronary
artery diseases using machine learning approach", IOP Conf. Ser. Mater. Sci. Eng., vol. 1099, no. 1, p.
012068, 2021.
[http://dx.doi.org/10.1088/1757-899X/1099/1/012068]
[8] N. Pradhan, G. Rani, V.S. Dhaka, and R.C. Poonia, "Diabetes prediction using artificial neural
network", In: Deep Learning Techniques for Biomedical and Health Informatics. Elsevier, 2020, pp.
327-339.
[9] M.G. Oza, G. Rani, and V.S. Dhaka, "Glaucoma detection using convolutional neural networks", In:
Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning. IGI
Global, 2021, pp. 1-7.
[http://dx.doi.org/10.4018/978-1-7998-2742-9.ch001]
[10] H.B. McMahan, E. Moore, D. Ramage, and S. Hampson, "Communication-efficient learning of deep
networks from decentralized data", In: Artificial Intelligence and Statistics. pp. 1273-1282. PMLR,
2017.
[11] J. Konečný, B. McMahan, and D. Ramage, "Federated Optimization: Distributed optimization beyond
the datacenter", arXiv, vol. 1511, p. 03575, 2015.
[12] T.S. Brisimi, R. Chen, T. Mela, A. Olshevsky, I.C. Paschalidis, and W. Shi, "Federated learning of
predictive models from federated Electronic Health Records", Int. J. Med. Inform., vol. 112, pp. 59-67,
2018.
[http://dx.doi.org/10.1016/j.ijmedinf.2018.01.007] [PMID: 29500022]
[13] L. Huang, and D. Liu, "Patient clustering improves efficiency of federated machine learning to predict
mortality and hospital stay time using distributed electronic medical records", J. Biomed. Inform,
2019. Available at: http://arxiv.org/abs/1903.09296(2019).
[14] LT Phong, Y Aono, T Hayashi, L Wang, and S Moriai, "Privacy-preserving deep learning via
additively homomorphic encryption", IEEE Trans Info. Forensics. Security., vol. 13, no. 5, pp. 133345, 2018.
[http://dx.doi.org/10.1109/TIFS.2017.2787987]
[15] A. Bhowmick, J. Duchi, J. Freudiger, G. Kapoor, and R. Rogers, "Protection against reconstruction
and its applications in private federated learning", ArXiv Preprint arXiv, vol. 1812, 2018p. 00984.
16 Disease Prediction using Machine Learning Rani et al.
https://t.me/med1917
[16] Q. Dou, T.Y. So, M. Jiang, Q. Liu, V. Vardhanabhuti, G. Kaissis, Z. Li, W. Si, H.H.C. Lee, K. Yu, Z.
Feng, L. Dong, E. Burian, F. Jungmann, R. Braren, M. Makowski, B. Kainz, D. Rueckert, B. Glocker,
S.C.H. Yu, and P.A. Heng, "Federated deep learning for detecting COVID-19 lung abnormalities in
CT: a privacy-preserving multinational validation study", NPJ Digit. Med., vol. 4, no. 1, p. 60, 2021.
[http://dx.doi.org/10.1038/s41746-021-00431-6] [PMID: 33782526]
[17] D. Yang, Z. Xu, W. Li, A. Myronenko, H.R. Roth, S. Harmon, S. Xu, B. Turkbey, E. Turkbey, X.
Wang, W. Zhu, G. Carrafiello, F. Patella, M. Cariati, H. Obinata, H. Mori, K. Tamura, P. An, B.J.
Wood, and D. Xu, "Federated semi-supervised learning for COVID region segmentation in chest CT
using multi-national data from China, Italy, Japan", Med. Image Anal., vol. 70, no. 101992, p. 101,
2021.
[http://dx.doi.org/10.1016/j.media.2021.101992] [PMID: 33601166]
[18] R. Kumar, A.A. Khan, J. Kumar, Zakria, N.A. Golilarz, S. Zhang, Y. Ting, C. Zheng, and W. Wang,
"Blockchain-federated-learning and deep learning models for COVID-19 detection using CT
imaging", IEEE Sens. J., vol. 21, no. 14, pp. 16301-16314, 2021.
[http://dx.doi.org/10.1109/JSEN.2021.3076767] [PMID: 35789224]
[19] W. Zhang, T. Zhou, Q. Lu, X. Wang, C. Zhu, H. Sun, Z. Wang, S.K. Lo, and F.Y. Wang, "Dynamic
fusion-based federated learning for COVID-19 detection", IEEE Internet Things J., vol. 8, no. 21, pp.
15884-15891, 2021.
[http://dx.doi.org/10.1109/JIOT.2021.3056185] [PMID: 35663640]
[20] L. Zhang, B. Shen, A. Barnawi, S. Xi, N. Kumar, and Y. Wu, "FedDPGAN: Federated Differentially
Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia", Inf.
Syst. Front., vol. 23, no. 6, pp. 1403-1415, 2021.
[http://dx.doi.org/10.1007/s10796-021-10144-6] [PMID: 34149305]
[21] I. Feki, S. Ammar, Y. Kessentini, and K. Muhammad, "Federated learning for COVID-19 screening
from Chest X-ray images", Appl. Soft Comput., vol. 106, no. 107330, p. 107330, 2021.
[http://dx.doi.org/10.1016/j.asoc.2021.107330] [PMID: 33776607]
[22] B. Liu, B. Yan, Y. Zhou, Y. Yang, and Y. Zhang, "Experiments of federated learning for COVID-19
chest X-ray images", Advances in Artificial Intelligence and Security. pp. 41–53, 2021.
[23] Available at: https://github.com/lindawangg/COVID-Net/blob/master/docs/COVIDx.md(2021).
[24] Available at: https://github.com/ieee8023/covid-chestxray-dataset
[25] Available at: https://github.com/agchung/Figure1-COVID-chestxray-dataset
[26] Available at: https://github.com/agchung/Actualmed-COVID-chestxray-dataset
[27] Available at: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
[28] Available at: https://nihcc.app.box.com/v/ChestXray-NIHCC
[29] Available at: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70230281
[30] K. Simonyan, and A. Zisserman, "Very deep convolutional networks for large-scale image
recognition", arXiv preprint arXiv, vol. 1409, 2014p. 1556.
[31] A. Krizhevsky, I. Sutskever, and G.E. Hinton, "ImageNet classification with deep convolutional neural
networks", Commun. ACM, vol. 60, no. 6, pp. 84-90, 2017.
[http://dx.doi.org/10.1145/3065386]
[32] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition", Proceedings of
the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770-778. 2016.
[33] G. Huang, Z. Liu, L. van der Maaten, and K.Q. Weinberger, "Densely connected convolutional
networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp.
4700-4708, 2017.
Disease Prediction using Machine Learning, 2024, 17-30 17
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CHAPTER 2
Role of Artificial Intelligence in 3-D Bone Image
Reconstruction: A Review
Nitesh Pradhan3, Vijaypal Singh Dhaka1, Geeta Rani
1
Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur,
India
2
Dayanand Sagar University, Bangalore, Karnataka, India
3
LNM Institute of Information Technology, Jaipur, India
Abstract: Three-dimensional geometry of a bone is important in the correct diagnosis
of a disease, arthritis, or other bone deformities. The modalities such as Computer
Tomography Scans and Magnetic Resonance Imaging are used for a three-dimensional
view of a bone. Both the above- stated modalities have high costs and expose the
patient to strong carcinogenic radiations. Computer Tomography captures an extensive
number of images to collect the required information from a bone. Another modality
Magnetic Resonance Imaging is more suitable for retrieving information from soft
tissues rather than bones. Therefore, it becomes less effective to read the pathology
from bones. This has motivated the authors to identify imaging techniques useful in
detecting the pathology or deformity in bones. Also, this is the need of the hour to
provide a low cost and safer technique of bone imaging. To address this need, we
present a review of the bone imaging techniques and techniques applied for the
conversion of two dimensional images into three-dimensional form. We also give the
directions for developing the patient-specific and organ-specific optimized techniques
for 3-D reconstruction.
1,*
and Monika Agarwal
2
Keywords: Dual-energy, Deep learning, Deformation, Femur, Machine learning,
Three dimensional, X-ray.
INTRODUCTION
Three Dimensional (3-D) structure of an organ is important for the diagnosis of a
disease. The 3-D structure provides an exact configuration and orientation of the
organ. So, it is useful in the detection of disease in tissues or fractures in bones.
*
Corresponding author Geeta Rani: Department of Computer and Communication Engineering, Manipal University
Jaipur, Jaipur, India; E-mail: geetachhikara@gmail.com
Geeta Rani, Vijaypal Singh Dhaka & Pradeep Kumar Tiwari (Eds.)
All rights reserved-© 2024 Bentham Science Publishers
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This structure also gives information about chronic bone loss such as a glenoid
defect in recurrent shoulder dislocation and the extent of osteophytes in the
arthritic joint. Computer Tomography (CT) scan and Magnetic Resonance
Imaging (MRI) provide a 3-D configuration of a bone. But these techniques are
less preferred than 2-Dimensional (2-D) techniques due to their high cost and
heavy exposure to carcinogenic radiations [1].
The Dual-Energy X-ray Absorptiometry (DXA) images are used to diagnose
osteoporosis in patients. In the DXA image, the value of the T-score is presented.
The value of the T-score between +1 to -1 indicates healthy bone. Its value
between -1 to -2.5 shows that the bone has become prone to osteoporosis [2]. This
state is named osteopenia. A value below 2.5 is an indication of the poor quality
of a bone and a sign of osteoporosis. In this disease, there is a decrease in Bone
Mineral Density (BMD). Thus, it increases the risk of bone fracture. As per
details given in [3], in Europe, 30% of women of the age of 50 years suffer from
osteoporosis. As per the report published in the year 2000, 3.1 to 3.7 million cases
of osteoporosis were recorded. The direct cost paid for the treatment of this
disease was reported 32 billion [4]. As per the trend reported in a study [4], the
cost may rise to 76.8 billion per year in 2050.
X-ray imaging is a medical imaging technique used to capture bone deformities.
This technique shows a clear demarcation between the bones and soft tissues. So,
it becomes easy to read the information about bone deformity if any. X-ray
imaging is preferred over a CT scan in case of weight-bearing imaging and
dynamic imaging of joint motion (fluoroscopy). People prefer X-ray imaging due
to its high availability and low cost. Therefore, there is a requirement of proposing
a technique that can provide a 3-D view of a bone using 2-D imaging techniques
such as DXA and X-rays. The differences among the above-stated medical
imaging techniques are given in Table 1. The techniques for showing 3-D images
of bones or ways to reconstruct 3-D images from 2-D images are shown in Fig.
(1).
Table 1. Difference between CT scan, X-ray and DXA techniques.
Parameters CT Scan X-ray DXA
Radiation
Vulnerability
Cost
The effective radiation
dose from CT ranges from
2 to 10 millisievert (mSv)
[5].
Cost of CT Scan lies in the
range from $1,200 to
$3,200 [7].
Exposure to ionizing
radiation is about
0.1mSv [5].
The cost is from
$1200 to $4000 [7].
Exposure to ionizing radiation lies
in the range of 0.1µSv to 5µSv [6].
This is three times more costly than
the X-ray technique [7].
3-D Bone Image Reconstruction Disease Prediction using Machine Learning 19
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(Table 1) co nt.....
Parameters CT Scan X-ray DXA
Time taken for a
complete scan
Application
Takes about 5 minutes.
Suitable for bone injuries,
Lung and Chest imaging,
and cancer detection.
Takes a few seconds.
Useful to examine
fractured bones and
to diagnose diseases
in tissues.
Takes more time than X-ray
techniques but less time-consuming
than the CT scan technique.
Useful to diagnose osteoporosis or
measure mineral density of bone.
Fig. (1). Imaging in biomedical field images.
In this chapter, the authors present a review of 3-D and 2-D techniques used in
medical imaging as discussed in Table 1. This chapter also gives a comparative
analysis of techniques used for the 3-D reconstruction of bones from 2-D imaging
techniques.
ANALYSIS OF RELATED WORK
A review of related works shows that the 3-D reconstruction of medical images
from 2-D images attracted researchers to propose different models. The
contributions from the researchers are discussed below.
WEI et al. [1] identified that X-ray imaging is preferred over the CT scan image
due to the low intensity of exposure and low cost. Therefore, they proposed the 3D recreation system for the femoral shaft shape. They used an orthographical
heading for recreating a 3-D femur. They used a numerical morphology strategy
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for boundary detection. Further, they determined the central point of the femur
shaft edge as a pole datum line. Then they computed three coordinates of the pole
datum line by stamp point. Finally, their system displayed an enrolment of the 3D layout model and state of the femur shaft by creating the boundary. This
technique is useful to eliminate noise to enhance qualities of shapes but the
computational time is high for the complex structure of the bone shape.
Zhang et al. proposed an effective strategy for the 3-D recreation of femur bone
using Direct Linear Transformation (DLT) [8]. Their strategy uses two orthogonal
X-ray images anterior-posterior and lateral views to reconstruct the 3-D structure.
DLT does not require multiple images to calculate the distortion boundaries.
Therefore, it has a low computational expense and discovers its way into various
fields of use. The steps for 3-D reconstruction are shown in Fig. (2).
Fig. (2). Step by step process for reconstruction.
A.Le Bras et al. performed a comparison based examination of the 3-D
reconstruction of the proximal femur [9]. They used the 3-D CT scan remaking
method and a 3-D stereo radiographic reconstruction method [3]. In the 3-D CT
scan reconstruction, they considered the parameters viz. a scout view, volume,
image procurement, and recreation. They input these parameters to the ‘Slice
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Omatic’ software to obtain the 3-D reconstruction of the femur. In the 3-D stereo
radiographic reconstruction method, the low-measurement computerized X-ray
gadget performs a linear scanning of the femur in the posterior-anterior and lateral
views. This method employs the ‘NSCC algorithm’ to develop the 3-D
reconstruction. The authors [9], claimed that applying both the methods on 25
proximal femora gives a low value of mean P2S error. The error reported is less
than 2.0 mm. This proves the reliability of the 3-D stereo radiographic method of
reconstruction. The advantage of this strategy is, it very well may be utilized for
hard structures with a constant shape but this technique is tedious in terms of
computational cost.
Kyung Koh et al. [10], built the 3-D format model of the femur from a healthy
subject with ordinary stature and weight. They considered two X-ray images and
three CT scan images of five patients. They used a B Spline Free From
Deformation (FFD) to procure the patient-specific femur as shown in Fig. (3). To
control the deformation precision, FFD gives the facility to place the control
points at variable distance. Also the control points are easily manipulated by FFD
at a lower cost. On the other side, it is difficult to apply FFD technique on the
complex structures.
Patient
information
Three CT
images
X-ray images
Fig. (3). Spine FFD.
Cost function
Deformed Images
Template bone
Projection
images
Optimization
Cross-sectional
contours
Transform
Patient-specific
models
Femur 1 Femur 2
Femur 3 Femur 4
Femur5
Sonia Akkoul et al. [11], proposed a model for 3-D proximal femur surface
recreation. The model uses pseudo-stereo matching and 3-D points from the
highest quality level to decrease the non-availability of data. They used three
cadaveric proximal femora scanned with CT scans and X-ray images. They
followed a set of seven stages for the 3-D reconstruction of the proximal femur. In
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the first stage, they used the projection display to discover an angle between two
X-ray images of a femur. At the next stage, the system marks the boundary of the
femur using active contours. In the third stage, the system finds the coordinating
point between 2-D shapes using the Euclidian distance. In the next stage, the
system produces a 3-D point cloud. Now, it applies the Iterative Closed Point
(ICP) algorithm for 3-D rigid registration. In the last stage, the system applies a
meshing technique to find the 3-D shape of the proximal femur. The ICP
algorithm is straightforward and simple to implement but it calculates the starting
value of the iteration in the first step which should be correctly initialized
otherwise this algorithm suffers from the local optimum problem.
Sami P. Vaananen et al. [12] proposed a programmed technique for the
reconstruction of the 3-D shape. They applied Structured Auxiliary Mesh (SAM)
algorithm for this purpose as shown in Fig. (4). The authors used three sets of
bone images. The two sets of bone images were used for training the model and
the remaining third set of bone images was used as a testing dataset of the model.
Fig. (4). Development of SAM model.
N. Baka et al. [13] proposed the Statistical Shape Model (SSM) based technique
for the posture estimation and shape reconstruction of a 3-D bone surface using
two X-ray images. The SSM technique capture the global shape of the surface
without reducing the size of the object therefore SSM technique acquire high
space.
P.E. Galibarov et al. purposed the patients’ specific proximal femur surface
reconstruction model [14]. They applied their model to the planer radiographs.
The model completes the reconstruction in three steps. In the first step, they
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