Research on the Application of Artificial Intelligence in Disease

Prediction Using Medical Imaging

Jiarui Lai

Aberdeen Institute of Data Science and Artificial Intelligence, South China Normal University,

Guangzhou, Guangdong, 510631, China

Keywords: Artificial Intelligence, Medical Imaging, Computer-Aided Diagnosis, Disease Prediction, Deep Learning.

Abstract: The analysis of medical imaging data for disease diagnosis and prognosis has shown great potential for

artificial intelligence (AI). As AI continues to evolve, its impact on medical imaging is expected to grow,

opening up new possibilities for improved diagnostics, personalized treatment strategies, and ultimately,

better patient outcomes. An overview of the key applications of AI in this domain is provided in this article.

The article explains how deep learning, feature extraction, and image segmentation are some of the AI

techniques that might enhance computer-aided diagnosis and prognosis prediction from medical images. The

article also examines the challenges and considerations in translating AI-based solutions into clinical practice,

such as data quality, model interpretability, and regulatory approval. Finally, the article outlines future

research directions to improve the way AI is incorporated into medical imaging-based illness management

and prediction. So this article intends to offer researchers and clinicians an extensive understanding of AI

applications' current state and prospects in this rapidly evolving field.

1 INTRODUCTION

Medical imaging (MI) has a vital role in the clinical

workflow, providing valuable information for disease

diagnosis, treatment planning, and monitoring.

However, the interpretation of these complex medical

images often requires extensive expertise and can be

time-consuming, leading to potential delays in

diagnosis and treatment. However, AI methods, such

as image segmentation (IS), feature extraction (FE),

and deep learning (DL), can effectively enhance

computer-aided diagnosis and prognosis prediction

from medical images (Razzak, Imran, Naz, et al,

2018). These technologies have demonstrated

advantages in early identification, risk assessment,

and customized care planning across various disease

areas, including cancer, neurological disorders, and

cardiovascular diseases (Esteva, Kuprel, Novoa, et al,

2017). For example, a recent study by Zhang et al.

used DL to detect and categorize lung nodules in CT

scans, exceeding expert radiologists with a sensitivity

of 92% and specificity of 88% (Zhang, Zheng, Mak,

et al, 2016).

https://orcid.org/0009-0008-2965-2025

The importance of leveraging AI for medical

imaging analysis lies in Its capacity to improve

disease prediction and management. AI-based

solutions can assist clinicians by automating image

analysis, enhancing diagnostic accuracy, and

enabling personalized risk assessment and treatment

strategies. For instance, research by Wang et al.

demonstrated that a DL model for breast cancer

detection in mammograms obtained an area under the

curve (AUC) of 0.92, surpassing the performance of

radiologists (Wang, Peng, Lu, et al, 2017).

Despite the promising potential of AI in MI, the

translation of these technologies into clinical practice

faces several challenges. To guarantee the safe and

efficient integration of AI-based solutions in

healthcare settings, these issues must be resolved.

An extensive summary of the main uses of AI in

the study of medical imaging data for illness

diagnosis and prediction is given in this article. The

article discusses how AI techniques can be leveraged

to improve computer-aided diagnosis and prognosis

prediction. To be able to improve the integration of

AI in MI-based disease prediction and management.

The article also examines the challenges and

Lai and J.

Research on the Application of Artiﬁcial Intelligence in Disease Prediction Using Medical Imaging.

DOI: 10.5220/0013512000004619

In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning (DAML 2024), pages 165-170

ISBN: 978-989-758-754-2

165

considerations that need to be made when

implementing AI-based solutions in clinical settings.

Finally, it suggests future research possibilities.

2 THE TECHNOLOGY

PRINCIPLES AND BASIC

CONCEPTS OF AI IN

MEDICAL IMAGING

2.1 Image Segmentation

Medical IS, which entails dividing an image into

several significant regions or structures, such as

organs, tumors, or other anatomical structures—is an

essential activity. Precise segmentation is crucial for

numerous therapeutic uses, such as diagnosing

illnesses, organizing treatments, and tracking the

advancement of ailments. In the past, region-growing

algorithms, edge detection, and thresholding are

examples of common traditional segmentation

techniques. These techniques, however, might be

sensitive to image quality and anatomical variances

and frequently rely on hand-crafted features.

In recent years, the rapid advancements in AI,

particularly DL, have transformed the area of

segmenting MI. Comparing DL-based segmentation

techniques to conventional methods, the former have

shown to perform better, often achieving human-level

or even superhuman accuracy in various MI tasks.

Convolutional neural networks (CNNs), a kind of

deep neural network made to efficiently analyze and

extract features from image input, are the foundation

of DL-based IS. Large datasets of annotated medical

pictures, which give the ground truth segmentation

masks, are used to train these networks. The network

automatically extracts relevant features from the

input photos during training and converts them to the

right segmentation outputs.

One of the most popular DL architectures for

medical picture segmentation is the U-Net model

(Ronneberger, Olaf, Philipp, et al, 2015). In the U-

Net architecture, the decoder part reconstructs the

segmentation mask while the encoder part extracts

features from the input image. The skip connections

between the encoder and decoder layers allow the

network to effectively include both low-level and

high-level input, enabling precise localization and

segmentation of the target structures. Another

important concept in DL-based segmentation is

applying transfer learning, where a pre-trained model,

such as a model trained on natural images, is fine-

tuned on a specific medical imaging dataset. This

strategy can greatly improve the performance of the

segmentation model, especially when the available

medical image dataset is relatively small (Tajbakhsh,

Shin, Gurudu, et al,2016).

In addition to the architectural design of the

segmentation models, the efficiency of segmentation

based on DL is also greatly influenced by the

selection of optimization strategies and loss functions.

For instance, the Dice loss function has been

extensively utilized in medical picture segmentation

tasks. It quantifies the overlap between the ground

truth and the projected segmentation (Milletari,

Navab, and Ahmadi, 2016).

The application of medical IS using AI has led to

notable developments in some therapeutic areas. For

instance, within the domain of oncology, DL-based

segmentation of tumors in medical images has

enabled more accurate diagnosis, treatment strategy,

and oversight of cancer progression (Bi, Hosny,

Schabath, et al, 2019). Similarly, in the area of

neurology, AI-based segmentation of brain structures

has facilitated the early detection and monitoring of

neurological conditions like Alzheimer's (Wachinger,

Reuter, and Klein, 2018).

In conclusion, the principles and concepts

underlying AI-based IS in medical imaging have

revolutionized the field, enabling more accurate,

efficient, and reproducible analysis of MI. As the

field continues to evolve, the use of DL-based

segmentation techniques in clinical processes is

expected to significantly improve patient care and

outcomes.

2.2 Feature Extraction

The goal of FE is to identify and quantify the relevant

traits or patterns within an image that can be used to

inform diagnostic or prognostic decisions. In the

context of medical imaging, these features may

include anatomical structures, pathological lesions,

texture patterns, or other informative image

properties.

From raw picture data, DL models automatically

identify and extract pertinent characteristics. Unlike

traditional image processing techniques that depend

on manually engineered features, models for DL are

capable of learning hierarchical feature

representations directly from the input images. This

allows the models to capture complex, non-linear

relationships within the data that may be difficult to

define using predefined feature sets.

The basic workflow of AI-based FE in medical

imaging usually entails the actions listed below:

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1. Image preprocessing: The input images are

preprocessed to enhance relevant features, reduce

noise, and standardize the data format and resolution.

2. Model training: A DL model, is trained on a

large dataset of labeled MI. During training, the

model learns to extract features that are predictive of

the target labels, which may represent disease states,

anatomical structures, or other clinically relevant

information.

3. FE: The algorithm can be trained to extract

features from fresh, unexplored photos. The

activations of the intermediate layers of the CNN can

be used as feature representations, capturing different

levels of abstraction and complexity.

4. Feature selection and dimensionality reduction:

Depending on the specific application, the extracted

features may be further processed, such as by

selecting the most informative features or reducing

the dimensionality of the area of features using

methods such as t-SNE or principal component

analysis (PCA) (Maier-Hein, Eisenmann, Reinke, et

al, 2018).

5. Downstream analysis: The extracted features

can then be used as inputs to various machine learning

(ML) algorithms to support clinical decision-making

or research applications.

The accomplishment of FE in MI has been

demonstrated in numerous studies across various

domains, including disease detection and

classification, IS, and radionics analysis. For example,

Shen et al. used a DL framework to derive features

from lung CT scans and attained a high degree of

accuracy in diagnosing lung cancer (Shen, Margolies,

Rothstein, et al, 2019). Similarly, Bai et al. produced

a CNN-based model to segment the left ventricle of

the heart in cardiac MRI images, demonstrating the

potential of FE for quantitative analysis of anatomical

structures (Bai, Sinclair, Tarroni, 2018).

In conclusion, FE leverages the powerful

capabilities of DL models to automatically determine

and measure relevant image characteristics. By

capturing complex, non-linear patterns within the

data, these techniques possess the ability to enhance

the accuracy and efficiency of various medical and

scientific applications in the field of ML.

3 EXISTING TECHNIQUES AND

MODELS

U-Net Model The U-Net model is a CNN-based

segmentation architecture that has been widely

adopted in MI analysis (Litjens, Kooi, Bejnordi, et al,

2017). The key innovation of U-Net is its symmetric

encoder-decoder structure, which allows for the

effective combination of local and global information.

The contracting (encoder) path captures contextual

information. While accurate localization is made

possible by the expansive (decoder) path. Most

importantly, skip connections allow low-level

characteristics to flow more easily between the

encoder and decoder, allowing the network to

combine high-level semantic information with fine-

grained details. This unique design has proven to be

highly effective for segmenting complex medical

images, where both local and global features are

essential for the accurate delineation of anatomical

structures or pathological regions.

DL models, in contrast to conventional ML

techniques, are capable of autonomously extracting

hierarchical features from the input data without the

requirement for human feature engineering. CNNs, in

particular, have demonstrated cutting-edge results in

a variety of MI segmentation tasks, such as organ

segmentation, lesion detection, and cell instance

segmentation. The deep, multi-layer architecture of

CNNs allows them to seize complex visual patterns

and interactions between contexts within medical

images, resulting in segmentation findings that are

stronger and more precise.

Optimized Models with Before Processing and

Feature Selection (FS) To further improve the

effectiveness of segmentation models based on DL,

researchers have explored the integration of

traditional image processing techniques, such as pre-

processing and FS, with DL architectures (Kang,

Chang, Yoo, et al, 2018). Pre-processing steps, such

as image enhancement, noise reduction, and intensity

normalization, can help enhance the supplied data's

uniformity and quality, leading to more reliable FE

and segmentation. Additionally, the incorporation of

FS methods, which identify the most informative and

discriminative features, can help the DL model focus

on the most relevant information, thereby improving

its generalization and robustness. These hybrid

approaches leverage the strengths of both traditional

image processing and DL, resulting in more precise

and effective medical IS.

Multi-modal Fusion Segmentation Models

Medical imaging often involves the application of

several modalities, such as (CT, MRI, and PET, each

providing complementary information about the

anatomy and pathology of interest (Nie, Cao, Gao, et

al, 2016). Researchers have explored the fusion of

multi-modal medical imaging data to further enhance

the performance of segmentation models. By

integrating information from different imaging

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167

modalities, the segmentation accuracy can be

significantly improved, as each modality can

contribute unique and valuable insights about the

target structures. Multi-modal fusion segmentation

models leverage the complementary nature of the

input data, resulting in a more thorough and

trustworthy representation of the medical images—an

essential step in the procedures for patient oversight,

treatment strategy, and diagnosis.

In summary, the existing techniques and models

for medical IS and FE include the U-Net model, DL-

based segmentation models, optimized models with

pre-processing and FS, and multi-modal fusion

segmentation models. These approaches have

demonstrated significant advancements in the field,

leveraging the strengths of both DL and traditional

image processing techniques to achieve state-of-the-

art performance in a variety of medical imaging

applications.

4 REPRESENTATIVE

RESEARCH FINDINGS

The integration of advanced medical imaging

techniques with sophisticated computational methods

has been a driving force in the field of disease

prediction. Researchers have leveraged the wealth of

information contained within medical images to

develop innovative approaches for early detection,

diagnosis, and prognosis of various health conditions.

One well-known instance is the creation of Litjens

et al., who have explored the use of DL for the

automated analysis of MI. In their research, the team

developed deep CNNs that could be trained using

extensive medical image databases, such as those

obtained from MRI, CT, and histopathology. These

DL models were able to learn complex patterns and

features within the images, enabling them to

accurately detect and classify numerous illnesses,

including neurological conditions, cardiovascular

ailments, and cancer. Litjens and colleagues

demonstrated that their DL-based approach

outperformed traditional image analysis methods,

highlighting the power of these techniques in

extracting meaningful insights from the vast amount

of visual data available in medical imaging ((Litjens,

Kooi, Bejnordi, et al, 2017). They used a dataset

consisting of over 100,000 MRI, CT, and pathology

images and attained cutting-edge results in several

medical image analysis tasks by utilizing a model

based on deep CNNS. For example, the Dice

coefficient reached 0.96 in the liver segmentation task,

leading to better patient outcomes in the end.

One more noteworthy advancement in the field of

illness prediction using MI comes from the work of

Shen et al., who has centered on Alzheimer's disease

early diagnosis (Shen, Margolies, Rothstein, et al,

2019). Alzheimer's is a devastating

neurodegenerative disorder, and early diagnosis is

crucial for implementing effective interventions.

Shen and colleagues created a framework using DL

that could analyze brain MRI scans to identify subtle

changes in brain structure and function, which are

often indicative of the onset of Alzheimer's disease.

They achieved a precision of 85% when forecasting

the progression of Alzheimer's disease through the

developed DL framework using large-scale brain

MRI scan data from over 1,000 subjects, even in the

early stages, providing clinicians with valuable

information for personalized treatment planning.

Furthermore, Ardila et al. have made important

contributions to the area of lung cancer detection

using chest X-ray images (Ardila, Kiraly, Bharadwaj,

et al, 2019). One of the main causes of cancer-related

mortality is lung cancer, and increasing patient

survival rates requires early identification. Ardila and

colleagues used over 100,000 chest X-ray images in

the development of their DL model and achieved an

F1-score of 0.90 in the lung nodule detection task,

which is comparable to the diagnostic level of

radiologists, demonstrating the potential of these

techniques to support the early detection of lung

cancer. In addition to these groundbreaking studies,

researchers have also explored the incorporation of

multi-modal medical imaging data for disease

prediction. For instance, Esteva et al. has investigated

the use of both dermatological images and genomic

data to improve the accuracy of skin cancer

classification (Esteva, Kuprel, Novoa, et al, 2017).

They used a classification model combining over

120,000 dermatopathology images with

corresponding genomic data to achieve an accuracy

of 72% in skin cancer diagnosis tasks, outperforming

models solely using visual information or genomic

data. By combining the visual information from skin

lesion images with the genetic data of patients, the

researchers were able to develop more

comprehensive and accurate predictive models for

different types of skin cancer, including melanoma.

By utilizing the strength of extensive medical

image datasets and sophisticated algorithms, these

researchers have paved the way for more accurate,

personalized, and early-stage disease detection,

eventually enhancing patient outcomes and changing

the medical environment.

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5 CHALLENGES AND

CONSIDERATIONS

Although AI-powered technologies hold great

potential to improve patient outcomes, optimize

treatment plans, and increase diagnostic accuracy,

many significant aspects must be properly taken into

account. The quality and bias of data is a major

challenge when it comes to using AI in healthcare

contexts. The caliber and representativeness of the

training data have a major impact on the effectiveness

of AI models. Data-driven biases, including regional,

socioeconomic, and demographic biases, might result

in the creation of models that worsen or maintain

current healthcare inequities. To reduce bias and

promote fair access and outcomes for all patients, it is

imperative to ensure diversity and inclusivity in the

data used to train AI systems. The interpretability and

explainability of AI models is an important factor to

take into account. Many cutting-edge AI algorithms

make it challenging for clinicians to understand the

reasoning behind the model's predictions or decisions.

The integration of AI into clinical workflows may be

hampered by this lack of transparency, as medical

practitioners may be reluctant to depend on systems

they do not completely understand. Creating AI

models that are easier to understand and comprehend

is a top objective to overcome this difficulty.

The regulatory landscape for the approval and

deployment of AI-based medical devices and

software also presents a significant hurdle. Existing

regulatory frameworks, such as those established by

the U.S. Food and Drug Administration (FDA) or the

European Medicines Agency (EMA), were primarily

designed for traditional medical devices and may not

adequately address the unique characteristics of AI

systems, which can be continuously updated and

refined (Gerke, Babic, Evgeniou, et al, 2020).

Navigating the regulatory approval process for AI-

powered tools is crucial to ensuring patient safety and

building trust in these technologies within the medical

community.

Furthermore, the application of AI in therapeutic

practice raises important ethical concerns, such as the

necessity for strong data privacy and security

measures, the possibility that AI will worsen already-

existing healthcare inequities, and the potential

impact on the patient-clinician relationship (Topol,

2019). Addressing these ethical concerns through the

development of robust governance frameworks and

ongoing stakeholder engagement is essential to

guarantee the fair and responsible application of AI in

healthcare.

6 CONCLUSION

This article has offered an extensive overview of the

principles, techniques, and models underlying the

integration of AI and MI for disease prediction. The

research has highlighted the notable developments in

areas such as IS, FE, and the development of DL-

based models, which have revolutionized the area of

image analysis in medicine.

The key conclusions drawn from this study are

that AI-powered tools, particularly DL models, have

shown superior execution in a range of medical

imaging tasks compared to traditional approaches.

From unprocessed image data, these algorithms can

automatically learn and extract pertinent elements,

enabling more accurate detection, diagnosis, and risk

assessment of various health conditions. What’s more,

the findings of this research are consistent with and

build upon the existing body of work in the field of

AI and MI for disease prediction. Further

understanding of the significance of model

interpretability, data quality, and multi-modal data

source integration is provided by the study, all of

which are essential for the effective use of these

technologies in clinical practice.

The challenges of AI and MI also face some

problems and challenges. These include concerns

about data bias, the need for interpretable and

explainable AI models, regulatory hurdles, and

ethical considerations related to the equitable

deployment of these technologies. In future research

directions must to concentrate on addressing the

identified challenges and further exploring the

integration of AI and medical imaging for

personalized disease prediction and prevention. This

may include the creation of stronger and inclusive

training datasets, the design of interpretable AI

models, and the investigation of multi-modal data

fusion techniques to enhance the predictive

capabilities of these systems.

The findings of this research have significant

practical implications for the healthcare industry. The

ability to leverage AI and medical imaging for early

disease detection and personalized risk assessment

can result in better patient outcomes, lower medical

expenses, and more effective resource use.

Healthcare providers should consider incorporating

these technologies into their clinical workflows to

enhance their diagnostic and preventive capabilities.

In summary, the findings suggest that these

technologies hold immense promise in transforming

the way of approaching early detection, risk

assessment, and personalized treatment strategies,

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169

ultimately leading to a more proactive and efficient

healthcare system.

A major advancement in the realm of healthcare

is the combination of AI and MI in the prognosis of

disease. By continuing to push the boundaries of what

is possible, it can work toward a future where disease

prediction and prevention are more accurate,

personalized, and accessible to all.

REFERENCES

Ardila, D., Kiraly, A.P., Bharadwaj, S., Choi, B., Reicher,

J.J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado,

G. and Naidich, D.P., 2019. End-to-end lung cancer

screening with three-dimensional deep learning on low-

dose chest computed tomography. Nature medicine,

25(6), pp.954-961.

Bai, W., Sinclair, M., Tarroni, G., Oktay, O., Rajchl, M.,

Vaillant, G., Lee, A.M., Aung, N., Lukaschuk, E.,

Sanghvi, M.M. and Zemrak, F., 2018. Automated

cardiovascular magnetic resonance image analysis with

fully convolutional networks. Journal of cardiovascular

magnetic resonance, 20(1), p.65.

Bi, W.L., Hosny, A., Schabath, M.B., Giger, M.L., Birkbak,

N.J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh,

C., Dunn, I.F. and Mak, R.H., 2019. Artificial

intelligence in cancer imaging: clinical challenges and

applications. CA: a cancer journal for clinicians, 69(2),

pp.127-157.

Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M.,

Blau, H.M. and Thrun, S., 2017. Dermatologist-level

classification of skin cancer with deep neural networks.

nature, 542(7639), pp.115-118.

Gerke, S., Babic, B., Evgeniou, T. and Cohen, I.G., 2020.

The need for a system view to regulate artificial

intelligence/machine learning-based software as

medical device. NPJ digital medicine, 3(1), p.53.

Kang, E., Chang, W., Yoo, J. and Ye, J.C., 2018. Deep

convolutional framelet denosing for low-dose CT via

wavelet residual network. IEEE transactions on medical

imaging, 37(6), pp.1358-1369.

Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi,

F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken,

B. and Sánchez, C.I., 2017. A survey on deep learning

in medical image analysis. Medical image analysis, 42,

pp.60-88.

Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S.,

Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H.,

Bradley, A.P., Carass, A. and Feldmann, C., 2018. Why

rankings of biomedical image analysis competitions

should be interpreted with care. Nature

communications, 9(1), p.5217.

Milletari, F., Navab, N. and Ahmadi, S.A., 2016, October.

V-net: Fully convolutional neural networks for

volumetric medical image segmentation. In 2016 fourth

international conference on 3D vision (3DV) (pp. 565-

571). Ieee..

Nie, D., Cao, X., Gao, Y., Wang, L. and Shen, D., 2016.

Estimating CT image from MRI data using 3D fully

convolutional networks. In Deep Learning and Data

Labeling for Medical Applications: First International

Workshop, LABELS 2016, and Second International

Workshop, DLMIA 2016, Held in Conjunction with

MICCAI 2016, Athens, Greece, October 21, 2016,

Proceedings 1 (pp. 170-178). Springer International

Publishing.

Razzak, Muhammad Imran, Saeeda Naz, and Ahmad Zaib.

"Deep learning for medical image processing:

Overview, challenges and the future." Classification in

BioApps: Automation of decision making (2018): 323-

350.

Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-

net: Convolutional networks for biomedical image

segmentation." Medical image computing and

computer-assisted intervention–MICCAI 2015: 18th

international conference, Munich, Germany, October 5-

9, 2015, proceedings, part III 18. Springer International

Publishing, 2015.

Shen, L., Margolies, L.R., Rothstein, J.H., Fluder, E.,

McBride, R. and Sieh, W., 2019. Deep learning to

improve breast cancer detection on screening

mammography. Scientific reports, 9(1), p.12495.

Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., Hurst, R.T.,

Kendall, C.B., Gotway, M.B. and Liang, J., 2016.

Convolutional neural networks for medical image

analysis: Full training or fine tuning?. IEEE

transactions on medical imaging, 35(5), pp.1299-1312.

Topol, E.J., 2019. High-performance medicine: the

convergence of human and artificial intelligence.

Nature medicine, 25(1), pp.44-56.

Wachinger, C., Reuter, M. and Klein, T., 2018. DeepNAT:

Deep convolutional neural network for segmenting

neuroanatomy. NeuroImage, 170, pp.434-445.

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M. and

Summers, R.M., 2017. Chestx-ray8: Hospital-scale

chest x-ray database and benchmarks on weakly-

supervised classification and localization of common

thorax diseases. In Proceedings of the IEEE conference

on computer vision and pattern recognition (pp. 2097-

2106).

Zhang, R., Zheng, Y., Mak, T.W.C., Yu, R., Wong, S.H.,

Lau, J.Y. and Poon, C.C., 2016. Automatic detection

and classification of colorectal polyps by transferring

low-level CNN features from nonmedical domain.

IEEE journal of biomedical and health informatics,

21(1), pp.41-47.

DAML 2024 - International Conference on Data Analysis and Machine Learning

170