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3-D Bone Image Reconstruction Disease Prediction using Machine Learning 23
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extract the contours of the femur from the planer radiograph. In the second step,
they perform the coordinating process on the extracted contours. In the last step,
they reconstruct the 3-D geometry.
P. Gamage et al. proposed a technique for the 3-D remaking of the patientspecific bone model from 2-D radiographs [15]. They extracted the edge points
from 2-D X-ray images to determine the boundary of the femur. Now, they
applied for a non-rigid registration between the edges recognized in the
radiograph. They projected the contour point of the genetic model. Now, the
translational field distinguishes the deformation required by the 3-D anatomical
model in the anterior and lateral viewpoint. In the last step, an entire 3-D
translational field is developed through a thin plate spline (TSP) based on
insertion and the 3-D generic anatomical data. The TSP technique does not
include surface patches in their calculation which gives robust results. On the
other hand, if the data size is too large then it leads to the issue of edge points.
Tristan Whitmarsh et al. [16], purposed the reconstruction model for the 3-D
shape of the proximal femur from a single DXA. This technique uses a statistical
model applied to a large data set of Quantitative Computerized Tomography
(QCT) scans.
Table 2. Performance comparison of 3-D reconstruction models on different image types.
Image Used Average Error Root Mean Square Error (RMS) Number of Projections
Single DXA image [10] 1.1mm 2.6mm 1
X-ray image [17] 1.2mm
Two DXA image [28]
0.8mm 2.1mm 2
2.8mm
2
Guoyan Zheng et al. proposed the Partial Least Square Regression (PLSR) model
for the 3-D reconstruction of volumetric intensity from 2-D X-ray images [20]. In
this technique, the authors used Independent Statistical Shape and Displacement
and Appearance (DA) approach. The PLSR technique can easily handle the multicollinearity between the independent points at the time of 3-D reconstruction. On
the other side, PLSR needs a large number of data for training.
Moon Kyu Lee et al. created a 3-D model for a finished femoral bone [21]. The
authors used a regular X-ray image and combined the anatomical parameters such
as neck length, femoral length, head offset length, the anatomical axis, and
sagittal radius into a referential 3-D shape. They calculated the internal position
and range of the femoral head by nonlinear regression. They determined the
center point of the anatomical axis by applying the elliptical regression.
24 Disease Prediction using Machine Learning Pradhan et al.
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Haithem Boussaid et al. [22] proposed a model for the 3-D reconstruction of the
proximal femur using low-dose biplanar X-ray images.
S. Laporte et al. worked on the 3-D reconstruction model by detecting the
contours from bi-planar radiographs [23]. They employed Direct Linear
Transformation (DLT) and Non-Stereorediography Corresponding Points
algorithm (NSCP) for the reproduction of the structure. NSCP fails in the
structures such as the knee joint. Therefore, the authors applied Non-Stereo
Corresponding Contours (NSCC) algorithm for recreating the 3-D shape of the
bone.
Ryo Kurazume et al. proposed a technique for the 3-D reconstruction of a femoral
shape using a parametric approach [24]. They performed the statistical analysis of
3-D femoral shape obtained from CT scan images of 56 patients. They employed
the manual segmentation and marching cubes algorithm, Principal Component
Analysis (PCA) [25].
S. Kolta et al. [26] proposed the model for the 3-D reconstruction of the proximal
femur bone from the DXA image. The model works by detecting the contours in
the images.
V.H. Kim et al. used the Simulated Implantation System (SIS) [27] for 3-D
perception and numeric investigations between the artificial hip joint and the
femur [28, 29]. The Architecture of 3-D Reconstruction Simulation Systems is
shown in Fig. (5).
Fig. (5). Architecture of 3-D reconstruction simulation systems.
Vikas Karade et al. [30] proposed a Laplacian Surface Deformation (LSD) based
technique for 3-D femur reconstruction from bi-planer X-ray images.
3-D Bone Image Reconstruction Disease Prediction using Machine Learning 25
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Moritz Ehlke et al. introduced a GPU-based method to deal with render virtual Xray projections of deformable tetrahedral networks for 3-D geometry
reconstruction from X-ray [31].
Guoyan Zheng et al. introduced a 2-D to 3-D correspondence building technique
by point matching procedure in the non-rigid 2-D structures [17]. They employed
the adapted iterative closest point (ICP) algorithm [32] at the first stage and
statistical instantiation at the second phase. The authors applied the regularized
shape deformation at the third stage [33].
The above discussion shows that many researchers proposed the techniques for
the reconstruction of 3-D medical images [1 - 9]. These techniques focus on the
reconstruction of the femur bone. The above-stated techniques are based on
numerical morphology strategy for boundary detection [1], DLT [8], 3-D stereo
radiographic reconstruction [9], B spline FFD [10], SSM [7], contour detection
[8], and edge detection [9] for the reconstruction of 3-D view of femur bone from
the 2-D images. These techniques report low accuracy due to the ineffectiveness
in detecting the edges or boundaries. The performance comparison of the most
effective techniques is given in Table 3.
Table 3. Comparative analysis of methods of 3-D reconstruction.
S.No
1
2
3
4
5
6
7
8
9
10
11
Author
Karade
et al. [30]
Karade
et al. [30]
Filippi
et al. [34]
Gunay
et al. [35]
Baka
et al. [13]
Zhu
et al. [36]
Fleute
et al. [37]
Gamage
et al. [38]
Laporte
et al. [39]
Tang
et al. [40]
Le Bras.
et al. [9].
Method
LSD-SOM
LSD-SOM
FFD
FFD Tibia Real - - - 100 AMD, 1GHz
SSM
SSM
SSM
TPS
NSCC
Hybrid
atlas
NSCC
Shape
Distal
femur
Distal
femur
Full
Femur
Distal
Femur
Distal
Femur
Distal
Femur
Full
femur
Distal
femur
Distal
Femur
Proximal
Femur
Input
Real Manual 5 1.2 1.4 52
Simulated
Simulated Manual 5 1.4 - - -
Real
Real Manual 10 0.9 178
Simulated Automatic - - 1.0 60 -
Real Automatic 6 0.9 - - -
Real Semi-automatic 8 - 1.4 - -
Simulated Automatic 2 2.0 - - -
Real Manual 28 2.0 - - -
Contour
Generations
Automatic
Automatic
(canny)
Number
of Cases
Mean
Error
22
10 - 1.68 300 2.4 GHz, 2GB
P2S
1.2
RMS
P2SErrror
1.5
Computation
Time (S)
46
System
Configuration
Intel. 2.4 GHz,
Intel. 2.4 GHz,
Intel 2.67
GHz, 9GB
8GB
8GB
26 Disease Prediction using Machine Learning Pradhan et al.
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(Table 3) co nt.....
S.No Author Method Shape Input
12
13
14
15
16
17
18
S. Kolta
et al.
[26].
Ryo
et al.
[24].
Gyoyan
et al. [17]
Galibarov
et al.
[14].
Tristan
et al.
[16].
Sonia
et al.
[11].
Sami
et al.
[12].
NSCC
SSM
SSM
SSM
SSM
SSM
SAM
Proximal
femur
Proximal
Femur
Femur
Proximal
femur
Proximal
femur
Proximal
Femur
Proximal
femur
Real Manual 25 0.8 2.1 600 -
Real Manual 56 1.1 - - -
Simulated
Real Manual 32 3.04 - 300 4 CPU, 3GHz
Real Automatic 115 1.1 2.6 - -
Simulated Automatic 3 0.89 1.37 - -
Real Manual 83 1.42 - - -
Contour
Generations
automatic
Number
of Cases
Mean
Error
-
P2S
1.1
RMS
P2SErrror
1.4
Computation
Time (S)
-
System
Configuration
-
Furthermore, the existing techniques can be categorised into three classes namely
manual, simulated and automatic based on the methodology used for the contour
detection. The analysis of these techniques as given in Table 3, clearly shows that
the automatic techniques report the minimum value of Root Mean Square (RMS)
P2S Error and Mean Square (MS) P2S error. The automatic techniques also take
less time for contour detection. Thus, these techniques are adopted for a 3-D
reconstruction.
However, the automation of 3-D reconstruction has been observed in the
literature, but the systems are not intelligent. These systems do not learn from the
error generated. So, there is a low scope of reducing the error. Moreover, the
existing techniques solely rely on the input dataset available or data generated by
simulation. The techniques are incapable to generate the dataset if a small dataset
is given as input.
CONCLUSION
In this chapter, we present a review of the techniques used for 3-D reconstruction
from 2-D medical imaging. The researchers worked for the conversion of 2-D
femur bones into its corresponding 3-D structure. These techniques adopt three
modes for the reconstruction viz. manual, simulated, or automatic. The automatic
techniques outperform the manual and simulated techniques in terms of MS P2S
error, RMS P2S error, and computation time, as shown in Table 3. But the
3-D Bone Image Reconstruction Disease Prediction using Machine Learning 27
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automatic techniques lack in self -generation of the dataset in case a small dataset
size is available. Also, these techniques lack in learning from the generated error.
This reduces the scope of improvement in the techniques.
Based on the above discussion, we conclude that the automatic techniques can be
adopted for the 3-D reconstruction of the femur from its 2-D images. But, there is
a huge scope of developing the automatic and intelligent techniques for
reconstruction of 3-D structure from the 2-D medical images. The use of
optimized neural network architectures such as Generative Adversarial Networks
(GAN) can resolve the challenges of manual, simulated as well as automatic
techniques used for 3-D reconstruction. We observed from the review of existing
techniques that there is a scope in developing the organ-specific, patient-specific,
automatic, and intelligent models for 3-D reconstruction. The use of machine
learning and deep learning can be game changers in this application. Also, there is
a huge scope in applying optimization algorithms for improving the efficiency and
accuracy of 3-D reconstruction models.
REFERENCES
[1] W. Wei, G. Wang, and H. Chen, "3D reconstruction of a femur shaft using a model and two 2D X-ray
images", 4th International Conference on Computer Science & Education, pp. 720-722, 2009.
[http://dx.doi.org/10.1109/ICCSE.2009.5228123]
[2] R. Smith-Bindman, J. Lipson, R. Marcus, K.P. Kim, M. Mahesh, R. Gould, A. Berrington de
González, and D.L. Miglioretti, "Radiation dose associated with common computed tomography
examinations and the associated lifetime attributable risk of cancer", Arch. Intern. Med., vol. 169, no.
22, pp. 2078-2086, 2009.
[http://dx.doi.org/10.1001/archinternmed.2009.427] [PMID: 20008690]
[3] L.J. Melton III, E.A. Chrischilles, C. Cooper, A.W. Lane, and B.L. Riggs, "How many women have
osteoporosis? JBMR anniversary classic. JBMR, Volume 7, Number 9, 1992", J. Bone Miner. Res.,
vol. 20, no. 5, pp. 886-892, 2005.
[http://dx.doi.org/10.1359/jbmr.2005.20.5.886] [PMID: 15931736]
[4] J.A. Kanis, and O. Johnell, "Requirements for DXA for the management of osteoporosis in Europe",
Osteoporos. Int., vol. 16, no. 3, pp. 229-238, 2005.
[http://dx.doi.org/10.1007/s00198-004-1811-2] [PMID: 15618996]
[5] Available at: https://safety.unimelb.edu.au/__data/assets/pdf_file/0011/4722761/ ionising-radiatio-
-management-guidelines.pdf
[6] S. Mastrogiacomo, W. Dou, J.A. Jansen, and X.F. Walboomers, "Magnetic resonance imaging of hard
tissues and hard tissue engineered bio-substitutes", Mol. Imaging Biol., vol. 21, no. 6, pp. 1003-1019,
2019.
[http://dx.doi.org/10.1007/s11307-019-01345-2] [PMID: 30989438]
[7] J. Pirayesh Islamian, I. Garoosi, K. Abdollahi Fard, and M.R. Abdollahi, "Comparison between the
MDCT and the DXA scanners in the evaluation of BMD in the lumbar spine densitometry", Egypt. J.
Radiol. Nucl. Med., vol. 47, no. 3, pp. 961-967, 2016. [http://dx.doi.org/10.1016/j.ejrnm.2016.04.005].
[http://dx.doi.org/10.1016/j.ejrnm.2016.04.005]
[8] B. Zhang, S. Sun, J. Sun, Z. Chi, and C. Xi, "3D reconstruction method from biplanar radiography
using DLT algorithm: Application to the femur", First International Conference on Pervasive
Computing, Signal Processing and Applications, pp. 251-254, 2010.
28 Disease Prediction using Machine Learning Pradhan et al.
https://t.me/med1917
[http://dx.doi.org/10.1109/PCSPA.2010.68]
[9] A. Le Bras, S. Laporte, V. Bousson, D. Mitton, J.A. De Guise, J.D. Laredo, and W. Skalli,
"Personalised 3D reconstruction of proximal femur from low-dose digital biplanar radiographs", Int
Congress Series, vol. 1256, pp. 214-219, 2003.
[http://dx.doi.org/10.1016/S0531-5131(03)00285-1]
[10] K. Koh, Y.H. Kim, K. Kim, and W.M. Park, "Reconstruction of patient-specific femurs using X-ray
and sparse CT images", Comput. Biol. Med., vol. 41, no. 7, pp. 421-426, 2011.
[http://dx.doi.org/10.1016/j.compbiomed.2011.03.016] [PMID: 21641590]
[11] S. Akkoul, A. Hafiane, R. Leconge, K. Harrar, E. Lespessailles, and R. Jennane, "3D reconstruction
method of the proximal femur and shape correction", 2014 4th International Conference on Image
Processing Theory, Tools and Applications (IPTA), pp. 1-6, 2014.
[http://dx.doi.org/10.1109/IPTA.2014.7001939]
[12] S.P. Väänänen, L. Grassi, G. Flivik, J.S. Jurvelin, and H. Isaksson, "Generation of 3D shape, density,
cortical thickness and finite element mesh of proximal femur from a DXA image", Med. Image Anal.,
vol. 24, no. 1, pp. 125-134, 2015.
[http://dx.doi.org/10.1016/j.media.2015.06.001] [PMID: 26148575]
[13] N. Baka, B.L. Kaptein, M. de Bruijne, T. van Walsum, J.E. Giphart, W.J. Niessen, and B.P.F.
Lelieveldt, "2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using
statistical shape models", Med. Image Anal., vol. 15, no. 6, pp. 840-850, 2011.
[http://dx.doi.org/10.1016/j.media.2011.04.001] [PMID: 21600836]
[14] P.E. Galibarov, P.J. Prendergast, and A.B. Lennon, "A method to reconstruct patient-specific proximal
femur surface models from planar pre-operative radiographs", Med. Eng. Phys., vol. 32, no. 10, pp.
1180-1188, 2010.
[http://dx.doi.org/10.1016/j.medengphy.2010.08.009] [PMID: 20933453]
[15] P. Gamage, S.Q. Xie, P. Delmas, and P. Xu, "3D reconstruction of patient specific bone models from
2D radiographs for image guided orthopedic surgery", In: 2009 Digital Image Computing: Techniques
and Applications Melbourne, VIC, Australia, 2009.
[http://dx.doi.org/10.1109/DICTA.2009.42]
[16] T. Whitmarsh, L. Humbert, M. De Craene, L.M. Del Rio Barquero, and A.F. Frangi, "Reconstructing
the 3D shape and bone mineral density distribution of the proximal femur from dual-energy X-ray
absorptiometry", IEEE Trans. Med. Imaging, vol. 30, no. 12, pp. 2101-2114, 2011.
[http://dx.doi.org/10.1109/TMI.2011.2163074] [PMID: 21803681]
[17] G. Zheng, S. Gollmer, S. Schumann, X. Dong, T. Feilkas, and M.A. González Ballester, "A 2D/3D
correspondence building method for reconstruction of a patient-specific 3D bone surface model using
point distribution models and calibrated X-ray images", Med. Image Anal., vol. 13, no. 6, pp. 883-899,
2009.
[http://dx.doi.org/10.1016/j.media.2008.12.003] [PMID: 19162529]
[18] S. Filippi, B. Motyl, and C. Bandera, "Analysis of existing methods for 3D modelling of femurs
starting from two orthogonal images and development of a script for a commercial software package",
Comput. Methods Programs Biomed., vol. 89, no. 1, pp. 76-82, 2008.
[http://dx.doi.org/10.1016/j.cmpb.2007.10.011] [PMID: 18093692]
[19] G. Zheng, "3d volumetric intensity reconsturction from 2d x-ray images using partial least squares
regression", 2013 IEEE 10th International Symposium on Biomedical Imaging, pp. 1268-1271, 2013.
San Francisco, CA, USA.
[http://dx.doi.org/10.1109/ISBI.2013.6556762]
[20] M.K. Lee, S.H. Lee, A. Kim, I. Youn, T.S. Lee, N. Hur, and K. Choi, "The study of femoral 3D
reconstruction process based on anatomical parameters using a numerical method", J. Biomech. Sci.
Eng., vol. 3, no. 3, pp. 443-451, 2008.
[http://dx.doi.org/10.1299/jbse.3.443]
3-D Bone Image Reconstruction Disease Prediction using Machine Learning 29
https://t.me/med1917
[21] H. Boussaid, S. Kadoury, I. Kokkinos, J.Y Lazennec, G. Zheng, and N. Paragios, "3D Model-based
reconstruction of the proximal femur from low-dose biplanar x-ray images", British Machine Vision
Conference, 2011.
[http://dx.doi.org/10.5244/C.25.35]
[22] S. Laporte, W. Skalli, J.A. De Guise, F. Lavaste, and D. Mitton, "A biplanar reconstruction method
based on 2D and 3D contours: application to the distal femur", Comput. Methods Biomech. Biomed.
Engin., vol. 6, no. 1, pp. 1-6, 2003.
[http://dx.doi.org/10.1080/1025584031000065956] [PMID: 12623432]
[23] R. Kurazume, K. Nakamura, T. Okada, Y. Sato, N. Sugano, T. Koyama, Y. Iwashita, and T.
Hasegawa, "3D reconstruction of a femoral shape using a parametric model and two 2D fluoroscopic
images", Comput. Vis. Image Underst., vol. 113, no. 2, pp. 202-211, 2009.
[http://dx.doi.org/10.1016/j.cviu.2008.08.012]
[24] H. Chui, and A. Rangarajan, "A new point matching algorithm for non-rigid registration", Comput.
Vis. Image Underst., vol. 89, no. 2-3, pp. 114-141, 2003.
[http://dx.doi.org/10.1016/S1077-3142(03)00009-2]
[25] S. Kolta, A. Le Bras, D. Mitton, V. Bousson, J.A. de Guise, J. Fechtenbaum, J.D. Laredo, C. Roux,
and W. Skalli, "Three-dimensional X-ray absorptiometry (3D-XA): A method for reconstruction of
human bones using a dual X-ray absorptiometry device", Osteoporos. Int., vol. 16, no. 8, pp. 969-976,
2005.
[http://dx.doi.org/10.1007/s00198-004-1782-3] [PMID: 15599494]
[26] Y-H. Kim, J-K. Kim, and K. Choi, "Three-dimensional reconstruction of human femur using
consecutive computer tomography images and simulated implantation system", J. Med. Eng. Technol.,
vol. 28, no. 5, pp. 205-210, 2004.
[http://dx.doi.org/10.1080/0309190021000059722] [PMID: 15370999]
[27] A. Watt, and F. Policarpo, The Computer Image. Addison-Wesley: Reading, MA, 1998.
[28] R. C. Gonzalez, and R. E. Woods, Digital Image Processing. Addison-Wesley: Reading, MA, 1992.
[29] V. Karade, and B. Ravi, "3D femur model reconstruction from biplane X-ray images: A novel method
based on Laplacian surface deformation", Int. J. CARS, vol. 10, no. 4, pp. 473-485, 2015.
[http://dx.doi.org/10.1007/s11548-014-1097-6] [PMID: 25037878]
[30] M. Ehlke, H. Ramm, H. Lamecker, H.C. Hege, and S. Zachow, "Fast generation of virtual X-ray
images for reconstruction of 3D anatomy", IEEE Trans. Vis. Comput. Graph., vol. 19, no. 12, pp.
2673-2682, 2013.
[http://dx.doi.org/10.1109/TVCG.2013.159] [PMID: 24051834]
[31] G. Zheng, S. Gollmer, S. Schumann, X. Dong, T. Feilkas, and M.A. González Ballester, "A 2D/3D
correspondence building method for reconstruction of a patient-specific 3D bone surface model using
point distribution models and calibrated X-ray images", Med. Image Anal., vol. 13, no. 6, pp. 883-899,
2009.
[http://dx.doi.org/10.1016/j.media.2008.12.003] [PMID: 19162529]
[32] A. Guéziec, P. Kazanzides, B. Williamson, and R.H. Taylor, "Anatomy-based registration of CT-scan
and intraoperative X-ray images for guiding a surgical robot", IEEE Trans. Med. Imaging, vol. 17, no.
5, pp. 715-728, 1998.
[http://dx.doi.org/10.1109/42.736023] [PMID: 9874295]
[33] K.T. Rajamani, M.A. Styner, H. Talib, G. Zheng, L.P. Nolte, and M.A.G. Ballester, "Statistical
deformable bone models for robust 3D surface extrapolation from sparse data", Med. Image Anal., vol.
11, no. 2, pp. 99-109, 2007.
[http://dx.doi.org/10.1016/j.media.2006.05.001] [PMID: 17349939]
[34] M. Gunay, M.B. Shim, and K. Shimada, "Cost- and time-effective three-dimensional bone-shape
reconstruction from X-ray images", Int. J. Med. Robot., vol. 3, no. 4, pp. 323-335, 2007.
30 Disease Prediction using Machine Learning Pradhan et al.
https://t.me/med1917
[http://dx.doi.org/10.1002/rcs.162] [PMID: 18200623]
[35] Z. Zhu, and G. Li, "Construction of 3D human distal femoral surface models using a 3D statistical
deformable model", J. Biomech., vol. 44, no. 13, pp. 2362-2368, 2011.
[http://dx.doi.org/10.1016/j.jbiomech.2011.07.006] [PMID: 21783195]
[36] P. Gamage, S.Q. Xie, P. Delmas, and P. Xu, "3D reconstruction of patient specific bone models from
2D radiographs for image guided orthopedic surgery", In: 2009 Digital Image Computing: Techniques
and Applications. IEEE: Melbourne, VIC, Australia, 2009, pp. 212-216.
[http://dx.doi.org/10.1109/DICTA.2009.42]
[37] T.L. Bredbenner, T.D. Eliason, R.S. Potter, R.L. Mason, L.M. Havill, and D.P. Nicolella, "Statistical
shape modeling describes variation in tibia and femur surface geometry between control and incidence
groups from the osteoarthritis initiative database", J. Biomech., vol. 43, no. 9, pp. 1780-1786, 2010.
[http://dx.doi.org/10.1016/j.jbiomech.2010.02.015] [PMID: 20227696]
[38] M. Fleute, and S. Lavallée, "Nonrigid 3-D/2-D registration of images using statistical models",
International Conference on Medical Image Computing and Computer-Assisted Intervention. 1999,
pp. 138-147 Berlin, Heidelberg.
[http://dx.doi.org/10.1007/10704282_15]
[39] T.S. Tang, and R.E. Ellis, "2D/3D deformable registration using a hybrid atlas", International
Conference on Medical Image Computing and Computer-Assisted Intervention. 2005, pp. 223-230,
Berlin, Heidelberg.
[http://dx.doi.org/10.1007/11566489_28]
[40] M.K. Garg, and S. Kharb, "Dual energy X-ray absorptiometry: Pitfalls in measurement and
interpretation of bone mineral density", Indian J. Endocrinol. Metab., vol. 17, no. 2, pp. 203-210,
2013.
[http://dx.doi.org/10.4103/2230-8210.109659] [PMID: 23776890]
Disease Prediction using Machine Learning, 2024, 31-51 31
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CHAPTER 3
Role of Machine Learning and Deep Learning
Techniques in Detection of Disease Severity: A
Survey
Geeta Rani1, Vijaypal Singh Dhaka1* and Sushma Hans
1
Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur,
India
2
Amity University, Dubai Campus, Dubai, United Arab Emirates
Abstract: The increasing number of health issues is a cause of concern for public as
well as health services across the globe. However, a boom in the use of imaging
techniques such as CT scans and chest radiographs has been observed for correct
diagnosis. But, manual scanning of these modalities requires expertise in modality
reading. It is also a time-consuming task. Artificial intelligence-based techniques have
proven their potential in pattern recognition, object identification, and data analysis.
Therefore, these techniques can be used to provide assisting tools for the primary
screening of diseases from these modalities. It has been observed from the literature
that a lot of research works are available on disease diagnosis and classification using
machine learning, and deep learning. But, the disease severity detection is
underexplored. Moreover, the techniques employed for the detection of the severity of
diseases have lacunae that need immediate attention. These challenges motivated us to
review the machine learning and deep learning-based technological solutions proposed
in the literature for the detection of disease severity. The objective of this research is to
present a comprehensive survey of research works available about disease severity
detection. This research also presents a comparative analysis of the machine learning
techniques and deep learning techniques employed, datasets used, and performance
achieved. It also highlights the drawbacks of the technological solution proposed.
Further, it provides the directions for future scope in the domain of disease severity
detection.
2
Keywords: Artificial intelligence, Disease, Deep learning, Machine learning,
Severity.
*
Corresponding author Vijaypal Singh Dhaka: Department of Computer and Communication Engineering, Manipal
University Jaipur, Jaipur, India; E-mail: vijaypalsingh.dhaka@jaipur.manipal.edu
Geeta Rani, Vijaypal Singh Dhaka & Pradeep Kumar Tiwari (Eds.)
All rights reserved-© 2024 Bentham Science Publishers
32 Disease Prediction using Machine Learning Rani et al.
https://t.me/med1917
INTRODUCTION
The onset of COVID-19 in the year 2020 enforced us to rethink about the
importance of individual as well as community’s health. Also, it worked as a
driving force to investigate the quick and effective means of disease diagnosis.
Thus, a huge change in the health infrastructure, disease diagnosis techniques, and
human resource management is observed. The technological solutions have been
proposed as an alternative to traditional laboratory tests to minimize the cost of
disease diagnosis and delay in report generation.
The study of the literature reveals the potential of Machine Learning (ML) and
Deep Learning (DL) techniques in image quality enhancement [1, 2], pattern
recognition, and image reconstruction [3, 4]. Thus, these techniques have been
widely used for disease screening in human beings [5 - 7] as well as plants [8].
In recent years, the detection of the severity of diseases has gained significant
attention from the research community. Detection of the severity of a disease at an
early stage is essential to reduce its impact on the patients and mortality risks in
patients. Many patients may miss the correct time of treatment as only a few
symptoms appear at an early stage of disease. Therefore, there is a need to utilize
the potential of computer-aided automatic diagnostic methods for detecting
disease severity.
The degree of accuracy of severity prediction using traditional laboratory tests is
highly dependent on the methods of sample collection, sample storage,
transportation, and equipment used. Also, these techniques are inconvenient to
patients, incur high costs, and lead to delays in severity detection. Thus, it may
increase the mortality rate. These challenges motivated us to explore the
technological solutions available for the detection of disease severity.
The comprehensive study of available research works shows a significant
contribution from the research community in the arena of machine learning and
deep learning for disease severity detection. Indeed, various ML techniques have
been employed for severity detection when parametric data is available in the
form of reports, ECG signals, clinical features, etc. [10 - 16]. The researchers
employed the techniques such as K-Nearest Neighbour (KNN), Support Vector
Machine (SVM), Random Forest (RF), Multi-tree XGBoost model and AdaBoost.
The comparative analysis of these techniques is shown in the subsequent section
in Table 1. These techniques have their own significance based on the type of
dataset available and the result required. However, ML techniques reported the
highest accuracy of severity detection approximately 100% but there is a need to
improve the reliability and robustness of these techniques. Moreover, these
techniques require clinical data or data collected in terms of reports. The data
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