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Detection of Disease Severity Disease Prediction using Machine Learning 43
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dimension reduction, and classification for effective TB diagnosis. The input
images are preprocessed for removing the noises and artifacts. The system
extracted various features such as coverage, density, color histogram, area, length,
and texture feature in the segmentation phase. After feature extraction, the system
reduced the size of the feature vector using Principal Component Analysis (PCA).
The authors applied AFC-SVNN using the image-level features such as bacilli
count, bacilli area, scattering coefficients, and skeleton features to determine the
disease severity. The system determined the infection level using entropy, density,
and detection percentage. The authors worked on ZNSM-iDB dataset comprising
a collection of diverse smear microscopy digital images. They selected 75 images
from the dataset, out of which 50 images are bacilli and 25 images are non-bacilli
images. This technique is compared to various other machine learning techniques
such as SVM, NN, DT, RF, SVNN, and FC-SVNN and proved better than all
these techniques with an accuracy of 0.934. The drawback of this approach is that
it is implemented on a small dataset. Thus, it is less reliable for practical use.
Emrah Irmak proposed a fully automatic Convolutional Neural Network (CNN)
model for COVID-19 disease severity assessment. The model is trained and tested
using chest X-ray images labelled with four classes viz. mild, moderate, severe,
and critical [18]. In this study, COVID-19 severity score is determined based on
opacity degree and lung involvement. The author utilized nine datasets from
different publicly available sources to carry out this research. All images are
resized to 227 x 227 x 3 and are in color format. The images are randomly
separated as training, validation, and test sets in the ratio of 60:20:20. Initially, the
authors employed Grid Search Optimization (GSO) technique to decide the hyper
and architectural parameters of the CNN model. At the first step, GSO determined
16 weighted layers i.e. one input, three convolutions, three ReLU, one
normalization, three max-pooling, two fully connected, one dropout, one softmax,
and one classification layer. Later on, these hyper-parameters are tuned in the
second step. The classification is done using the CNN architecture and tuned
hyper-parameters in the third step. The performance of this model is evaluated
using the five-fold cross-validation procedure. The model achieved 95.52%
average classification accuracy after 240 iterations. The average AUC value of the
ROC curve is 0.9873. The proposed system differs from other techniques as it
performed disease detection as well as assessment. The proposed fully automatic
CNN method is more objective, faster, non-invasive, and does not require much
expertise and experience. The drawback of this approach is that it focuses mainly
on COVID-19. It lacks to assess other categories of pneumonia such as bacterial,
and non-COVID viral pneumonia.
Zhu et al. proposed a deep-learning CNN to stage lung disease severity of
COVID-19 infection on portable chest X-ray (CXR) [19]. The study is conducted
44 Disease Prediction using Machine Learning Rani et al.
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on the publicly available dataset with a sample size of 131 CXRs from 84
COVID-19 positive patients. A team of six chest radiologists score the CXR for
disease severity using the degree of opacity and geographical extent of the right
and left lung. The images are normalized, converted into RGB format, resized to
64x64 pixel images, and separated into training and testing datasets in the ratio of
80:20. The authors applied heuristics to initially generate a standard CNN model
for image analysis. Later on, the model is fine-tuned based on the performance of
the test dataset. This model used an additional regression layer to predict the
disease severity scores of CXR on a graded scale. The system applied five-fold
cross-validation and determines an optimum batch size of 8 and the model is
trained for 110 epochs. The model implemented mean squared error to measure
loss function as it predicted continuous values. The learning rate is set to 0.001
and Adam optimizer is used to minimize the loss function. This study proved that
transfer learning is better than traditional learning in the case of small datasets in
terms of performance and time taken. The first limitation of this work is the small
data size. Secondly, this study does not identify any correlation of radiographic
severity with clinical disease severity.
The authors of this paper proposed an Xception and ResNet50V2 based neural
network for the multiclass classification of chest X-ray images into three
categories viz. normal, pneumonia, and COVID-19 [20]. Reasonably, they had
very less images for the COVID-19 category, so they proposed a training method
for the unbalanced dataset. They used 633 images in 8 successive phases of the
training set. Among 633 images, they used 149 images from the COVID-19 class,
234 from pneumonia, and 250 from the normal class. The authors reported an
average accuracy of 99.50 for detecting COVID-19 cases. They reported an
average accuracy of 91.4% for all the three classes. To deal with the problem of
imbalanced dataset, the authors applied weighted loss for training the model.
Yuxin Zi explored deep metric learning to learn a distance function for calculating
the similarity between two X-ray images using the Siamese neural network [21].
Siamese neural networks consist of two identical networks which are common in
the same architecture and weights. The experimental results showed the
outperformance of the Siamese neural network. This network was able to extract
visualizable embeddings. Due to the incorporation of similarity information
between images, the authors reported more accurate class predictions. This study
is also useful in extracting information from similar patients. Thus, it is useful in
the prediction of disease and treatment planning.
Li et al. proposed a deep convolutional Siamese neural network approach to
evaluate disease severity in Retinopathy of Prematurity (ROP) in retinal
photographs and Osteoarthritis in knee radiographs [22]. The convolutional
Detection of Disease Severity Disease Prediction using Machine Learning 45
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Siamese network system was developed with two identical ResNet-101 subnetworks, with a contrastive loss function. The authors tested the framework on
two datasets containing 4861 images from 870 patients for evaluating ROP and
10,012 images from 3021 patients for the Osteoarthritis study. Each image in
ROP datasets is a retinal photograph of a single eye and segmented at retinal
vessels. Also, Knee radiographs contain bilateral knees and cropped for individual
left and right knees images as per the requirement for the input of the network.
The dataset is randomly partitioned into the ratio of 80:10:10 for training,
validation, and testing datasets respectively without overlapping. The authors
found a correlation between median Euclidean distance calculated over a pool of
randomly sampled images and disease severity rank for both ROP and knee
osteoarthritis. The system utilized the occlusion sensitivity map-based approach to
locate the disease and detect the site of change. They also showed that the
continuous detection of disease severity does not require any specific localization
of the pathology of interest such as tortuous vessels or image-joint space
narrowing. The system achieved the receiving operator characteristic area under
the curves of up to 0.90 on the test dataset for the evaluation of ROP or knee
osteoarthritis change. The authors found few limitations in this work. Firstly, the
labeling used for data is from ordinal disease severity class labels and not
assigned by the experts. Secondly, they worked upon 2-D data and felt the
extension of this approach on 3-D data. Lastly, the system can be tested upon
more loss functions such as triplet loss function, the marginal rank loss that may
improve the performance of the model.
Chang et al. proposed a convolutional Siamese network to identify knee pain
using MRI scans [23]. The system predicts pain by comparing information from
multiple 2-Dimensional (2-D) MRI slices from the knee with pain and the
contralateral knee of the same individual without pain. The system performed
Euclidean transformation to align the slices and cropped and resized the images to
224 x 224 pixels. The authors pre-processed the data manually for missing data or
abnormal misalignment within a slice. The dataset is divided in the ratio of
70:15:15 for training, validation, and testing datasets, respectively. The system
learned on a pair of MRI slices from the two knees that are extracted from a
relatively similar location within each knee. The system utilized class activation
maps because of its ability to localize the discriminative image regions from the
CNN model for classification without any prior locational information. They
performed a 10-fold cross-validation for the evaluation of the proposed system.
The system achieved an AUC of 0.808 and showed an improvement of 5.6%
when subjects with similar Western Ontario and McMaster Universities
Osteoarthritis (WOMUC) Index pain scores between two knees were excluded
with an AUC as 0.853. The proposed system still needs to be validated on
different imaging datasets for the full spectrum of OA.
46 Disease Prediction using Machine Learning Rani et al.
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Li et al. proposed a Siamese DNN to estimate Psoriasis Severity and locate the
skin lesion regions based on skin lesion images [24]. The proposed Psoriasis
Severity Evaluation Network (PSENet) is formed using two identical subnetworks. Each sub-network has a backbone network that works as a feature
extractor. The system takes an input image of fixed size as 800 x 1024. Later on,
the backbone network extracts the feature map with five resolutions. The network
also contains score refine modules that take these feature maps as input and
calculates the severity at varied granularities. The system utilized the Siamese
structure to determine the difference between two images while training. The
authors tracked the complete treatment processes of 1,787 Psoriasis patients and
built a dataset of 5,205 images. The labelling of the dataset is done by 11
professional dermatologists. The training is performed using the five-fold crossvalidation method. The authors chose ResNet-50 and GoogLeNet-v2 as
representative structures and used regression loss as their cost function. The
PSENet achieved the Mean Absolute Error (MAE) of 2.21 and an accuracy of
77.87%.
Zhang et al. developed a Siamese structure-based deep learning method to
automatically detect short-term lesion changes in melanoma screening [25]. The
system detected the lesion change by measuring the similarity between two
dermoscopy images taken for a lesion in short-time-frame. The authors proposed
a novel structure named Tensorial Regression Process under the Siamese
framework to extract the global features of lesion images along with a deep
ResNet convolutional process for local features. Two fully connected layers take
these concatenated global and local features as input and a softmax regression
layer is used to generate classification results as changed or unchanged. The
system implemented Tensorial Global Average Pooling (TGAP) for continuous
representation of data and to reduce the feature size. TGAP helps to reduce the
computational complexity of the system as the TGAP layer does not require any
parameter to be optimized. The system incorporated a segmentation mechanism as
a regularization term that helps in the decision-making process of dermatologists
who have the main focus on regions with specific patterns. The system was tested
on a dataset collected from Sydney Melanoma Diagnostic Centre (SMDC), Royal
Prince Alfred Hospital with 100,000 dermoscopy skin lesion images. The system
was trained for the entire dataset for 400 epochs. The learning rate was pre-set to
0.0001 and the regularization factor as 0.05. The ratio between training and test
sets was set to 8:2 and repeated 5-times. The system achieved an accuracy, AUC,
sensitivity, and specificity of 74.1%, 74.8%, 87.1%, and 66.8% respectively.
Wang et al. proposed a Siamese network-based model with gated feature fusion to
simultaneously assess the severity of knee OsteoArthritis (OA) [26]. The system
was tested on two publicly available double knee X-ray datasets i.e. OAI and
Detection of Disease Severity Disease Prediction using Machine Learning 47
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MOST datasets. The system performed data pre-processing [19] to unify all X-ray
images with the dark background and bright knees. Later on, all double-knee
images are divided into two single-knee images. All DICOM format images are
converted into the 8-bit unit of an image. Then, the system applied the histogram
equalization technique to all single-knee images. The system generated 24319 and
18634 single-knee images on the OAI and the MOST datasets respectively. The
system implemented two cascaded small convolutional networks that help in
locating knee joints accurately. The identified knee joints are cropped and splitted
into left and right patches according to their symmetry. These images are further
fed to the SE-ResBNext50-32x4d based Siamese network with shared weights
that extracts more detailed knee features. The adaptive gated feature fusion
method is implemented to record richer semantic information that improves
feature representation. The system also added knee OA/non-knee OA
classification tasks to enhance feature extraction. The training parameters of the
first level network of the knee joint detection model were pre-set. The model was
set executed for 10 epochs, with a learning rate of 0.001 and batch size of 500
images. Similarly, for the second level network, the learning rate was set to
0.0001 and batch size to 500. The model was executed for 10 epochs. The authors
introduced a new performance metric that is top±1 accuracy to assess KellgrenLawrence (KL) and OsteoArthritis Research Society International (OARSI) atlas
grades. Some of the additional features of OARSI such as medial tibial attrition,
medial tibial sclerosis, lateral femoral sclerosis are not considered for the
detection process. Also, this approach has considered knee joints detection and
grading prediction as separate steps. These steps could be combined as end-to-end
deep learning system.
Li et al. implemented a Convolutional Siamese Neural Network that calculates the
continuous radiography pulmonary disease severity score in COVID-19 [27]. This
score will be helpful in longitudinal disease evaluation and clinical risk
classification. The model was tested on the publicly available dataset, CheXpert
from Stanford Hospital, Palo Alto. The system used 161,590 anterior-posterior
images for training and also applied transfer learning on 314 frontal Chest X-Rays
(CXRs) from COVID-19 patients. The testing of the system was done on internal
and external test sets of 154 and 113 CXRs from different hospitals. The COVID19 dataset with 314 CXRs that are randomly partitioned into 9:1 for training and
validation set. The Convolutional Siamese network takes two separate images as
input and passes these images through identical networks with shared weights.
The sub-network is implemented using DenseNet121. The system used a two-step
training strategy. The first step includes training with weak labels on the large
CheXpert dataset using the contrastive loss function. The second step involves
transfer learning to the small COVID-19 training set with mean square error loss.
The model learned to differentiate between normal and abnormal lungs using
48 Disease Prediction using Machine Learning Rani et al.
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contrastive loss function. It learned to differentiate between the modified
Radiography Assessment of Lung Edema (mRALE) scores using the mean square
loss function. The implemented model is robust with similar performance in
image acquisition technique, the difference in x-ray machinery, and beam
penetration. The limitation of this approach is that this model is trained using
Anterior-Posterior (AP) chest radiographs rather than Posterior-Anterior (PA)
radiographs as it is more common. This has limited the generalizability of the
approach for PA radiographs and should be tested.
This is obvious from the above discussion that DL techniques play an important
role in the detection of the severity of a disease. These techniques are mainly
applied to medical images. The collection of medical images is more convenient
mode than blood or urine test in the laboratory. The comparative analysis of DL
techniques employed for disease severity detection is shown in Table 2.
Inference Deduced: The DL models such as VGG16, Xception Net, DNN, AFCSVNN, CNN, ResNet50V2, Siamese neural network, and Siamese DNN have
been applied on the CXRs, MRI, CT scan images for prediction of severity of
diabetic retinopathy, Parkinson’s disease, treatable blinding retinal diseases,
COVID-19, knee pain, retinopathy of prematurity, psoriasis severity, melanoma,
and pulmonary diseases. The applied models reported the accuracy in the range of
81.6% to 97.4%. However, these techniques are effective in severity detection,
there is a need to address the challenges such as class imbalance, overfitting, and
feature visualization. Also, there is huge scope to improve the reliability, and
robustness of the proposed models. Moreover, severity detection is applied for a
limited number of diseases. So, there is a possibility to extend the applications of
DL networks for severity detection of more number of diseases.
CONCLUSION
In this chapter, the authors studied the research works available in the literature to
recognize the role of machine learning and deep learning techniques in the
prediction of disease severity. They also presented the comparative analysis of
various ML and DL techniques proposed in the literature. Based on the study, it is
concluded that ML techniques are employed on the primary data such as age,
gender; clinical data obtained by blood tests, urine tests, ECG, etc. Further, it is
concluded that the parameters recorded by blood tests are more significant than
urine tests in identifying the disease severity. It is also concluded that the males
are more prone to severe infection of COVID-19.
Based on the comparative analysis of ML techniques, it is concluded that K-NN
and SVM dominate the other ML techniques employed for severity detection.
KNN reported the accuracy of approximately 100% for the detection of the
Detection of Disease Severity Disease Prediction using Machine Learning 49
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severity of heart failure. But, there is a need to improve the reliability, and
robustness of the approach by implementing it on a large dataset size collected
from various sources. Similarly, SVM reported the highest accuracy of 98.1% for
the detection of the severity of heart failure using ECG signals.
Now, based on the comparative analysis of DL techniques, it is concluded that
deep neural networks employed for disease severity prediction reported the
highest accuracy of 97.3% for the prediction of Diabetic Retinopathy. DL
techniques are easy to implement and useful for developing an intelligent tool for
assisting health experts in reading medical images. But, there is a need to design a
neural network that is self-sufficient to generate the labelled dataset using a small
dataset. Moreover, there is a scope to integrate the ML and DL techniques that
can analyse both the clinical and physiological parameters as well as medical
images for reliable and more accurate prediction of disease severity.
REFERENCES
[1] M Agarwal, G Rani, S Agarwal, and V. S. Dhaka, "Sequential model for digital image contrast
enhancement", Rec. Adv. Comp. Sci. Commun., vol. 14, no. 9, pp. 2772-2784, 2021.
[http://dx.doi.org/10.2174/2666255813999200717231942]
[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] N. Pradhan, V.S. Dhaka, G. Rani, and H. Chaudhary, "Machine learning model for multi-view
visualization of medical images", Comput. J., vol. 65, no. 4, pp. 805-817, 2022.
[http://dx.doi.org/10.1093/comjnl/bxaa111]
[4] N. Pradhan, V.S. Dhaka, G. Rani, and H. Chaudhary, "Transforming view of medical images using
deep learning", Neural Comput. Appl., vol. 32, no. 18, pp. 15043-15054, 2020.
[http://dx.doi.org/10.1007/s00521-020-04857-z]
[5] V.S. Dhaka, G. Rani, M.G. 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] [PMID: 33821094]
[6] G. Rani, M.G. Oza, and V.S. Dhaka, "Applying deep learning-based multi-modal for detection of
coronavirus", Multimedia Syst., vol. 28, no. 4, pp. 1251-1262, 2021.
[http://dx.doi.org/10.1007/s00530-021-00824-3] [PMID: 34305327]
[7] 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), pp. 764-769, 2020.
[http://dx.doi.org/10.1109/ICCMC48092.2020.ICCMC-000142]
[8] V.S. Dhaka, S.V. Meena, G. Rani, D. Sinwar, K. Kavita, M.F. Ijaz, and M. Woźniak, "A survey of
deep convolutional neural networks applied for prediction of plant leaf diseases", Sensors, vol. 21, no.
14, p. 4749, 2021.
[http://dx.doi.org/10.3390/s21144749] [PMID: 34300489]
[9] H. Yao, N. Zhang, R. Zhang, M. Duan, T. Xie, J. Pan, E. Peng, J. Huang, Y. Zhang, X. Xu, H. Xu, F.
Zhou, and G. Wang, "Severity detection for the coronavirus disease 2019 (COVID-19) patients using a
machine learning model based on the blood and urine tests", Front. Cell Dev. Biol., vol. 8, p. 683,
2020.
50 Disease Prediction using Machine Learning Rani et al.
https://t.me/med1917
[http://dx.doi.org/10.3389/fcell.2020.00683] [PMID: 32850809]
[10] L. Yan, "Prediction of criticality in patients with severe Covid-19 infection using three clinical
features: a machine learning-based prognostic model with clinical data in Wuhan", MedRxiv, 2020.
[http://dx.doi.org/10.1101/2020.02.27.20028027]
[11] G.A. Tadesse, T. Zhu, N. Le Nguyen Thanh, N.T. Hung, H.T.H. Duong, T.H. Khanh, P. Van Quang,
D.D. Tran, L.M. Yen, R. Van Doorn, N. Van Hao, J. Prince, H. Javed, D. Kiyasseh, L. Van Tan, L.
Thwaites, and D.A. Clifton, "Severity detection tool for patients with infectious disease", Healthc.
Technol. Lett., vol. 7, no. 2, pp. 45-50, 2020.
[http://dx.doi.org/10.1049/htl.2019.0030] [PMID: 32431851]
[12] E.E. Tripoliti, T.G. Papadopoulos, G.S. Karanasiou, K.K. Naka, and D.I. Fotiadis, "Heart failure:
diagnosis, severity estimation and prediction of adverse events through machine learning techniques",
Comput. Struct. Biotechnol. J., vol. 15, pp. 26-47, 2017.
[http://dx.doi.org/10.1016/j.csbj.2016.11.001] [PMID: 27942354]
[13] M. Leeza, and H. Farooq, "Detection of severity level of diabetic retinopathy using bag of features
model", IET Comput. Vis., vol. 13, no. 5, pp. 523-530, 2019.
[http://dx.doi.org/10.1049/iet-cvi.2018.5263]
[14] J.D. Bodapati, N.S. Shaik, and V. Naralasetti, "Composite deep neural network with gated-attention
mechanism for diabetic retinopathy severity classification", J. Ambient Intell. Humaniz. Comput., vol.
12, no. 10, pp. 9825-9839, 2021.
[http://dx.doi.org/10.1007/s12652-020-02727-z]
[15] S. Grover, "Predicting severity of Parkinson’s disease using deep learning", Procedia Comput. Sci.,
vol. 132, pp. 1788-1794, 2018.
[http://dx.doi.org/10.1016/j.procs.2018.05.154]
[16] D.S. Kermany, "Identifying medical diagnoses and treatable diseases by image-based deep learning",
Cell., vol. 172, no. 5, pp. 1122-1131, 2018.
[http://dx.doi.org/10.1016/j.cell.2018.02.010]
[17] R.S. Chithra, and P. Jagatheeswari, "Severity detection and infection level identification of
tuberculosis using deep learning", Int. J. Imaging Syst. Technol., vol. 30, no. 4, pp. 994-1011, 2020.
[http://dx.doi.org/10.1002/ima.22427]
[18] E. Irmak, "COVID-19 disease severity assessment using CNN model", IET Image Process., vol. 15,
no. 8, pp. 1814-1824, 2021.
[http://dx.doi.org/10.1049/ipr2.12153] [PMID: 34230837]
[19] J. Zhu, B. Shen, A. Abbasi, M. Hoshmand-Kochi, H. Li, and T.Q. Duong, "Deep transfer learning
artificial intelligence accurately stages COVID-19 lung disease severity on portable chest
radiographs", PLoS One, vol. 15, no. 7, p. e0236621, 2020.
[http://dx.doi.org/10.1371/journal.pone.0236621] [PMID: 32722697]
[20] M. Rahimzadeh, and A. Attar, "A modified deep convolutional neural network for detecting COVID-
19 and pneumonia from chest X-ray images based on the concatenation of Xception and
ResNet50V2", Informatics in Medicine Unlocked, vol. 19, p. 100360, 2020.
[http://dx.doi.org/10.1016/j.imu.2020.100360] [PMID: 32501424]
[21] Y. Zi, Deep Siamese Network for Metric Learning on Chest X-Ray Data. The University of Texas at
Dallas, 2019.
[http://dx.doi.org/10735.1/7204]
[22] M.D. Li, K. Chang, B. Bearce, C.Y. Chang, A.J. Huang, J.P. Campbell, J.M. Brown, P. Singh, K.V.
Hoebel, D. Erdoğmuş, S. Ioannidis, W.E. Palmer, M.F. Chiang, and J. Kalpathy-Cramer, "Siamese
neural networks for continuous disease severity evaluation and change detection in medical imaging",
NPJ Digit. Med., vol. 3, no. 1, p. 48, 2020.
[http://dx.doi.org/10.1038/s41746-020-0255-1] [PMID: 32258430]
Detection of Disease Severity Disease Prediction using Machine Learning 51
https://t.me/med1917
[23] G.H. Chang, D.T. Felson, S. Qiu, A. Guermazi, T.D. Capellini, and V.B. Kolachalama, "Assessment
of knee pain from MR imaging using a convolutional Siamese network", Eur. Radiol., vol. 30, no. 6,
pp. 3538-3548, 2020.
[http://dx.doi.org/10.1007/s00330-020-06658-3]
[24] Y. Li, "PSENet: Psoriasis Severity Evaluation Network", Proceedings of the AAAI Conference on
Artificial Intelligence, vol. 34, no. 1, 2020.
[http://dx.doi.org/10.1609/aaai.v34i01.5424]
[25] B. Zhang, Z. Wang, J. Gao, C. Rutjes, K. Nufer, D. Tao, D.D. Feng, and S.W. Menzies, "Short-term
lesion change detection for melanoma screening with novel siamese neural network", IEEE Trans.
Med. Imaging, vol. 40, no. 3, pp. 840-851, 2021.
[http://dx.doi.org/10.1109/TMI.2020.3037761] [PMID: 33180721]
[26] K. Wang, "A siamese network with adaptive gated feature fusion for individual knee OA features
grades prediction", Sci Rep., vol. 11, no. 1, p. 16833, 2021.
[http://dx.doi.org/10.1038/s41598-021-96240-8]
[27] 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]
[28] M.D. Li, N.T. Arun, M. Gidwani, K. Chang, F. Deng, B.P. Little, D.P. Mendoza, M. Lang, S.I. Lee, A.
O’Shea, A. Parakh, P. Singh, and J. Kalpathy-Cramer, "Automated assessment and tracking of
COVID-19 pulmonary disease severity on chest radiographs using convolutional siamese neural
networks", Radiol. Artif. Intell., vol. 2, no. 4, p. e200079, 2020.
[http://dx.doi.org/10.1148/ryai.2020200079] [PMID: 33928256]
52 Disease Prediction using Machine Learning, 2024, 52-79
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CHAPTER 4
Computer-aided Bio-medical Tools for Disease
Identification
E. Francy Irudaya Rani1, T. Lurthu Pushparaj2 and E. Fantin Irudaya Raj
1
Department of Electronics and Communication Engineering, Francis Xavier Engineering
3,*
College, Tirunelveli, Tamil Nadu, India
2
Department of Chemistry, Tirunelveli Dakshina Mara Nadar Sangam College, Tirunelveli, Tamil
Nadu, India
3
Department of Electrical and Electronics Engineering, Dr. Sivanthi Aditanar College of
Engineering, Thoothukudi, Tamil Nadu, India
Abstract: The health expert’s crucial task is to interpret the output and treat the disease
accordingly. They may delay the decision-making during emergencies. To address this
issue, research on smart tools for biomedical applications is much needed which may
help in making accurate decisions at the earliest stage. Discovery in medicinal research
requires state-of-the-art computer-based tools for diagnosing and treating complex
diseases such as cancer, COVID-19, SARS-Cov, MERS-Cov, tuberculosis, brain
disorders, heart, and lung-related chronic infections. Among various diagnostic
methods, image-based disease identification stands out as the most prominent approach
for detecting new and complex diseases. A well-trained computerized biomedical
system can provide physicians with enhanced support for early disease detection.
Biomedical images are typically acquired from various sources, including CT,
ultrasound, MRI, dermoscopy, X-ray, biopsy, and endoscopy. Presently, a wide range
of image-analysis procedures are available for biomedical images. These procedures
involve image acquisition, pre-processing, segmentation, feature extraction, and
classification, all contributing to improved disease decision accuracy. Although many
biomedical images are available online free of cost, the proper procedure must be
followed to select appropriate images from databases and enhance their quality. This is
important for effectively training image-processing algorithms and increasing their
efficiency. This leads to improved instrument performance and more valuable insights
into the diseases under study. It also handles complex and vast image data to detect
early signs of unusual signals, growth, inflammation, cell damage, protein sequence
changes, and blockages. Additionally, it should be user-friendly and convincing to
health experts to identify hidden biological issues. This chapter emphasizes the power
of computerized tools in image analysis and disease detection. It also focuses on recent
developments in the field of medicinal research.
*
Corresponding author E. Fantin Irudaya Raj: Department of Electrical and Electronics Engineering,
Dr. Sivanthi Aditanar College of Engineering, Thoothukudi, Tamil Nadu, India;
E-mail: fantinraj@gmail.com
Geeta Rani, Vijaypal Singh Dhaka & Pradeep Kumar Tiwari (Eds.)
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