Progressing Toward Smart Brain Hemorrhage Detection: Machine

Learning-Based Advanced Medical Imaging Technologies

Jingya Li

School of Computer Science, Fudan University, Shanghai, China

Keywords: Brain Haemorrhage Detection, Machine Learning, Deep Learning, Medical Imaging.

Abstract: In the rapidly evolving field of neuroscience, early and accurate detection of brain hemorrhage remains a

significant challenge with profound implications for patient outcomes. The integration of Machine Learning

(ML) techniques into diagnostic processes represents a promising frontier, offering the potential to

revolutionize how brain hemorrhages are identified and treated, thereby reducing the associated morbidity

and mortality rates. This review explores the application of ML in detecting brain hemorrhage. Recognizing

the significance of early and accurate detection, the review outlines the general ML workflow encompassing

data collection, preprocessing, model development, training, and evaluation. It delves into specific ML

methods, including traditional algorithms like Support Vector Machines (SVM) and Random Forests,

alongside deep learning approaches such as Recurrent Neural Networks (RNN) and Convolutional Neural

Networks (CNN), assessing their strengths and limitations. The discussion highlights key challenges faced by

ML in this context, such as the "black box" nature of models affecting interpretability, issues with

generalization across diverse datasets, and concerns surrounding data privacy. Proposed solutions and future

prospects are offered to address these challenges, emphasizing the potential of cascading models and the

importance of integrating more complex modeling techniques for improved clinical efficacy. This review

extensively discusses various machine learning algorithms and their application to brain hemorrhage detection,

aiming to drive improvements in ML and foster the integration of computer-aided diagnosis (CAD) in medical

imaging.

1 INTRODUCTION

Intracerebral hemorrhage (ICH), also known as brain

bleed, is a kind of stroke that occurs when there is

bleeding either between the brain tissue and the skull

or within the brain tissue itself. In the realm of

neuroscience, Intracerebral hemorrhage stands out as

a life-threatening condition, marked by a high fatality

rate and the potential for severe sequelae (Chen,

2024). Based on the urgency of symptoms and the

severity of consequences associated with ICH, it

becomes imperative to ensure the utmost accuracy in

examining, categorizing, and quantifying various

aspects of brain hemorrhages, including the critical

task of accurately gauging the volume and extent of

bleeding.

The diagnosis of brain hemorrhage commonly

relies on a variety of medical imaging techniques,

primarily utilizing Computed Tomography (CT) and

https://orcid.org/0009-0009-4832-9191

Magnetic Resonance Imaging (MRI). While both CT

and MRI exhibit high sensitivity in detecting brain

hemorrhages, the preference often leans towards CT,

especially in time-sensitive situations. This

inclination arises due to the quicker turnaround time

of CT scans, making them more suitable for patients

in critical conditions. Despite the widespread use of

MRI for detailed assessments, its extended scanning

duration may limit its applicability during the acute

phase (McGurgan, 2021).

While the comparison between CT and MRI

highlights their respective strengths and limitations,

even with accurate CT results, the intricate nature and

variability in brain hemorrhage imaging pose

significant challenges to manual diagnosis. However,

this is precisely where deep learning demonstrates its

prowess. Given the complexity and variations in these

images, deep learning algorithms excel in discerning

patterns and extracting relevant features, making

422

Li, J.

Progressing Toward Smart Brain Hemorrhage Detection: Machine Learning-Based Advanced Medical Imaging Technologies.

DOI: 10.5220/0012939500004508

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence (EMITI 2024), pages 422-428

ISBN: 978-989-758-713-9

them valuable tools in enhancing the accuracy and

efficiency of brain hemorrhage diagnosis.

In recognizing the importance of deep learning, it

is pivotal to position it within the broader context of

computer-aided detection (CAD) in the medical field.

Over the past few decades, the integration of CAD in

the analysis of medical datasets has become a

prominent area of research in medical imaging

(Gautam, 2021). This evolution has unfolded over a

span of time, gradually establishing CAD as a major

research focus. In the realm of clinical imaging

systems employing CAD, a spectrum of machine

learning algorithms is widely utilized, including

probability models like Naive Bayes and Gaussian

Mixture Model,as well as Support Vector Machine

(SVM), Artificial Neural Network (ANN), among

others.

In particular, machine learning algorithms based

on convolutional neural networks (CNNs) have

garnered significant attention. Leveraging their

exceptional feature learning and abstraction abilities,

remarkable achievements have been observed,

particularly in the segmentation of cerebral

hemorrhage in CT images (Qiu, 2019, Rao, 2021).

The utilization of CNNs in this context exemplifies

the potential of advanced machine learning

techniques in enhancing the accuracy and efficacy of

medical imaging analyses. Beyond these

advancements, ongoing research and exploration in

this field promise further innovations and

improvements in the diagnosis and understanding of

brain hemorrhages.

Overall, cerebral hemorrhages have received

comparatively less attention within the intersection of

AI and medicine, despite their medical significance.

However, recent years have witnessed substantial

progress, with an increasing number of studies and

algorithmic models significantly advancing the

accuracy, speed, and efficiency of ICH detection.

Thus, there is a crucial demand for a comprehensive

review within this specialized yet advancing field,

where AI converges with medicine.

The main objectives of this review encompass

providing a comprehensive overview of recent

advances in the application of deep learning

algorithms for the detection and classification of brain

hemorrhages. By scrutinizing diverse studies, the

emphasis lies in shedding light on the methodological

strides, performance benchmarks, and clinical

applicability of these technologies. Following this

introduction, the rest of this paper is organized as

follows. Afterward, it will proceed to detailed

analysis of various deep learning models with regard

to its design, training, and validation of brain

hemorrhage applications. The subsequent sections

will explore the inherent limitations and potential

challenges of these models, paving the way for a

comprehensive discussion on avenues for future

optimization and innovation.

2 METHODS

2.1 Framework of Machine

Learning-Based in Hemorrhage

Detection

Figure 1. illustrates the workflow for machine

learning and deep learning in intracranial hemorrhage

detection. The process begins with data collection,

followed by data preprocessing, data splitting, and

feature extraction. Subsequently, the selection and

construction of the model take place. Once the model

is established, it undergoes training, validation, and

testing phases. The model is then optimized through

result analysis and adjustment, preparing it for

deployment and application. Further details can be

found in the subsections below.

Figure 1: The workflow of Machine Learning (ML) and

Deep Learning (DL)-based in hemorrhage detection

(Photo/Picture credit: Original).

Dataset Collection. Robust and varied datasets

underpin the successful development of AI

algorithms for cerebral hemorrhage detection. While

exploring publicly available datasets used in the field

of cerebral hemorrhage detection, a prime example of

such resources is the dataset provided by the RSNA

Intracranial Hemorrhage Detection Competition on

Kaggle, which features brain CT images annotated

with hemorrhage conditions, serving as an invaluable

asset for research in this domain (Kaggle, 2020).

Collecting detailed information on available dataset

resources, including specific time frames, case types,

and slice thickness, is crucial. This not only enhances

data quality but also fosters a model's nuanced

understanding and detection capabilities.

Preprocessing. During the data preprocessing

phase shown in Figure 2, several techniques are

commonly employed to enhance image quality and

Progressing Toward Smart Brain Hemorrhage Detection: Machine Learning-Based Advanced Medical Imaging Technologies

423

optimize training outcomes, including image

denoising, which aims to reduce random variations

within images, and image enhancement methods like

contrast adjustment and edge enhancement to

improve visual clarity and highlight critical features.

Additionally, normalization and standardization

processes ensure the uniformity of image data in

terms of scale and value range. Furthermore, data

augmentation techniques such as rotation, scaling,

and flipping are utilized to introduce diversity into the

dataset. This is particularly crucial for deep learning

models, enabling them to learn a broader

representation of features.

Figure 2: The workflow of image preprocessing

(Photo/Picture credit: Original).

Data Splitting. Following preprocessing, data is

typically divided into training, validation, and testing

sets. This strategic segmentation is crucial for

assessing the model's performance and robustness,

ensuring it performs well not just on familiar data but

also on unseen datasets. For instance, a study in South

Korea on deep learning for detecting Acute

Intracranial Hemorrhage (AIH) stands out not only

for its collection of a large number of slices with

detailed cerebral hemorrhage information from

various medical institutions but also for its meticulous

categorization of data into three distinct datasets: a

development dataset, an external validation dataset,

and a reader study dataset. This approach not only

ensured the comprehensiveness of the datasets but

also laid a solid foundation for the enhancement of

the algorithm's accuracy and generalizability. A

noteworthy aspect of the research was the

adjudication of imaging standards via a tripartite

radiologist consensus, which bolstered the

annotation's accuracy and trustworthiness. This phase

is pivotal for the formulation of efficacious and

precise AI models since the caliber of annotations

directly correlates with the model's learning

efficiency (Yun, 2023).

Feature Extraction. Finally, in the feature

extraction phase, traditional machine learning

methods and deep learning approaches utilize manual

and automatic feature extraction, respectively. This

allows for the more effective capture and utilization

of key information within image data, enhancing the

model's ability to discern relevant patterns and

characteristics.

Model Training and Analysis. The subsequent

steps largely align with those typical of most machine

learning algorithm applications, which involve

selecting an algorithm to build the model. For

machine learning, this might include algorithms like

SVM and Random Forests, while for deeper learning,

this extends to ANN and CNN. Each of these

algorithms will be elaborated on in further sections.

Following this, the previously segregated training set

is utilized to train and test the model, adjusting

parameters such as the learning rate and the size of

hidden layers. Before deploying the model into a

clinical setting, it's crucial to compare and analyze the

model's performance shown in Figure 3, ensuring it

meets the necessary standards for accuracy and

reliability.

Figure 3: The workflow of model performance evaluation

(Photo/Picture credit: Original).

2.2 Machine Learning

Algorithms-Based Hemorrhage

Detection

2.2.1 SVM

Support Vector Machines are a supervised learning

algorithm well-suited for classifying high-

dimensional data, making them particularly valuable

in medical image analysis. For instance, a study from

Qingdao, China, utilized SVM among four machine

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learning models to construct a prognostic prediction

model for spontaneous cerebral hemorrhage

outcomes. The findings revealed that SVM

outperformed in overall predictive efficiency,

demonstrating significantly higher accuracy,

specificity, and sensitivity compared to other models

(Li, 2024). SVM's ability to tackle complex nonlinear

problems by selecting appropriate kernel functions

enables it to distinguish effectively between healthy

and damaged tissues in cerebral hemorrhage

detection.

2.2.2 Random Forest

Random Forest, is an ensemble learning technique

that utilizes multiple decision trees for classification

or regression analysis. This method selects random

data subsets and features for each tree during training,

with the final decision derived from a majority vote

or average of all trees' predictions. A study from

Beijing, China, showcased Random Forest's

effectiveness in predicting outcomes of cerebral

hemorrhage surgery, too. Using the Random Forest

model allowed for integrating extensive variables,

like patient condition changes and blood sugar levels

in this study, and therefore, the model demonstrated

high accuracy and consistent probability distribution

between the test and training sets against real-world

outcomes, highlighting its excellent calibration

capability (Gao, 2023). The robustness of Random

Forest in handling overfitting, along with its ability to

process substantial amounts of data, makes it an ideal

choice for classifying types of cerebral hemorrhage.

2.3 Deep Learning Algorithms-Based

Hemorrhage Detection

2.3.1 CNN

The Convolutional Neural Network depicted in

Figure 4 is tailored for the nuanced task of intracranial

hemorrhage detection from medical imaging.

Beginning with the input layer, the CNN processes

image data, extracting salient features through its

convolutional layers. Activation functions then

introduce non-linearity, allowing for complex

patterns to be captured, while pooling layers reduce

dimensionality, focusing on the most relevant

features. In the fully connected layers, the network

classifies the images, leveraging the distilled features

to accurately distinguish between hemorrhagic and

non-hemorrhagic cases. CNNs, in the context of

medical imaging analysis, have been pivotal, with

algorithms achieving accuracy rates above 99% in

some studies (Mahjoubi, 2023). The inherent

capability of CNNs to autonomously learn and refine

feature recognition empowers the model to uncover

potentially critical biomarkers for intracranial

hemorrhages that might have been previously

underestimated or missed by traditional analytical

methods. By harnessing the intricate feature detection

and classification capabilities of CNNs as outlined in

Figure 4, it is possible to achieve more nuanced and

precise identification of intracranial hemorrhages,

which is critical for timely and effective patient

treatment.

Figure 4: The structure and operation of CNN (Photo/

Picture credit: Original).

2.3.2 RNN

Recurrent Neural Networks (RNNs) are deep learning

models equipped with internal memory, making them

sensitive to sequential dependencies of events. Their

architecture allows them to apply the same operation

across each element in a sequence, where

computations for the current state are influenced by

both the present input and results from previous steps

(Fang, 2021). Although RNNs are not as

predominantly used in image analysis as CNNs, their

proficiency in handling sequential data offers

substantial benefits in specific scenarios related to

ICH detection. Particularly in analyzing time-series

medical imaging data, such as monitoring the

progression of bleeding or assessing treatment

effects, RNNs can account for temporal variations,

capturing changes in hemorrhagic areas over time.

3 DISCUSSION

3.1 Advantages and Disadvantages of

Traditional ML and DL

In the field of neuroscience, machine learning

technologies have demonstrated significant research

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Table 1: The Strengths and limitations of ML and DL.

ML (SVM, Random Forest) DL (RNN, CNN)

Limitations 1.Dependence on feature engineering.

2.Limited capability in handling high-dimensional data.

1.High demand for computational resources.

2.Poor interpretability.

Strengths 1.Interpretability.

2.Computational efficiency.

1.Automatic feature extraction.

2.Capability to handle complex patterns.

and application potential. Traditional ML methods

show their powerful capabilities in scenarios

involving smaller datasets with clear feature

structures, as visually summarized in Table 1. Their

prominent advantages lie in their high interpretability

and lower computational costs, which are particularly

important for foundational brain science research in

its exploratory stages. For instance, in preliminary

neuroimaging studies, researchers can use traditional

ML methods to intuitively and thoroughly analyze the

complex relationships between brain region activities

and behavioral responses. Meanwhile, deep learning

technologies, with their excellent ability to

automatically learn features, have shown unparalleled

performance in handling large and complex brain

imaging datasets.

However, as outlined in Table 1, both approaches

have their distinct limitations. Traditional machine

learning models often fall short in dealing with

problems involving nonlinear relationships, high-

dimensional features, and complex data structures,

where deep learning models tend to excel. On the

other hand, deep learning models, despite their

significant performance advantages, require

substantial amounts of training data and suffer from

interpretability issues due to their internal

complexity. These challenges are particularly

pronounced in the field of neuroscience, where

research demands not just high-precision predictive

outcomes but also a deep understanding of the

biological mechanisms behind these results. This

necessitates models that are not only accurate but also

possess a degree of interpretability.

In summary, the choice between machine learning

approaches hinges on the study's goals and the data's

nature and size. Traditional machine learning is suited

for early, small-scale studies with clear features, like

initial brain hemorrhage detection research, offering

ease of interpretation and lower computational needs.

Conversely, deep learning excels in analyzing

extensive datasets and complex patterns, crucial for

advanced brain hemorrhage analysis. Understanding

these methods' strengths and limitations is key to their

effective application in neuroscience, especially for

brain hemorrhage detection.

3.2 Challenges

3.2.1 Lack of Interpretability

The 'black box' nature of deep learning models poses

a significant challenge in neuroscience applications.

This opacity hinders the ability to understand and

explain the rationale behind a model's decisions,

posing problems for trust and validation in scientific

research. When models incorrectly identify or miss

brain hemorrhages, the lack of interpretability

complicates the process of debugging and refining

these algorithms to enhance their performance.

Furthermore, for applications as critical as medical

diagnostics, the inability to elucidate the decision-

making process can impede regulatory approval and

broader acceptance within the medical community.

3.2.2 Generalization Issues

Generalization issues challenge machine learning

models' efficacy in neuroscience due to the

significant variability in datasets, brain states, and

disease conditions. Differences in demographics,

genetic backgrounds, environmental factors, and

disease stages can impede a model's performance

across diverse populations. Additionally, variations

in brain imaging techniques and protocols introduce

further complexity. A study from Japan illustrates a

promising approach to overcoming these hurdles:

researchers developed machine learning predictive

models for hematoma expansion in acute

intracerebral hemorrhage, utilizing multicenter data

and multivendor CT images (Tanioka, 2022). While

this study demonstrates efforts to enhance model

generalizability and applicability across diverse

neurological conditions, it also underscores the

broader issue: the difficulty of developing models that

perform well across varied datasets, brain states, and

disease conditions. Generalization remains a

significant challenge in applying machine learning to

neuroscience.

3.2.3 Data Acquisition and Privacy

The creation and application of machine learning

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models, especially in neuroscience, demand large

datasets and substantial computational power. Yet,

the high costs of gathering quality data, alongside

privacy and ethical issues, restrict the formation of

extensive datasets, impeding the models' training and

validation process. Moreover, even well-trained ML

models face risks from various adversarial attacks,

such as membership, attribute, and model inversion

attacks, highlighting the crucial need for robust

privacy protection. A notable study introduced a

Phase, Guarantee, and Utility (PGU) triad-based

model after a comprehensive review, emphasizing the

importance of safeguarding data and privacy

throughout the ML process (Xu, 2021). Addressing

these challenges is a vital step for future exploration

and advancement in the field.

3.3 Future Prospects and Possible

Solutions

3.3.1 Linking ML Decisions to Their

Underlying Logic in ICH Detection

Addressing the black box issue in ML for ICH

detection involves enhancing model transparency and

interpretability, notably through integrating

explainable AI (XAI) techniques (Highton, 2023).

Methods like Layer-wise Relevance Propagation

(LRP) and SHAP (SHapley Additive exPlanations)

help visualize and understand influential features in

model predictions. Moreover, developing models

with inherently interpretable structures, such as

decision trees or Generalized Additive Models

(GAMs), allows for a direct understanding of how

inputs affect outputs. The black box issue in ML

transcends technical challenges, encompassing

ethical considerations as well. A study examines

model interpretability through the lens of four ethical

principles—autonomy, beneficence, non-

maleficence, and justice—to assess the necessity and

role of interpretability (Amann, 2020). These

solutions are crucial to ensure that developed models

are not only accurate but also understandable and

trustworthy for healthcare practitioners, integrating

ethical oversight into technological advancements.

3.3.2 Addressing Generalization in ICH

Detection via Transfer Learning and

Domain Adaptation

Incorporating transfer learning and domain

adaptation into ICH deep learning detection enhances

model generalization by utilizing knowledge from

extensive datasets, such as MRI or CT images, and

fine-tuning with a smaller, specific dataset for

hemorrhage detection. Transfer learning addresses

the scarcity of labeled data, while domain adaptation

further tailors models to align with target data

distributions, effectively managing discrepancies

caused by different imaging devices or protocols

across institutions (Xu, 2020).

3.3.3 Leveraging Federated Learning for

Brain Hemorrhage Detection

Incorporating big models into brain hemorrhage

detection, demands a nuanced approach to data

privacy and security. Federated learning emerges as a

pivotal solution in this context. It enables

decentralized model training, allowing for the

collaborative utilization of data across various

locations without the need for direct data exchange.

By ensuring that data remains local and only model

updates are shared, federated learning effectively

addresses privacy and security concerns, facilitating

the use of powerful computational models in sensitive

medical fields.

4 CONCLUSIONS

This article systematically explores the application of

ML in the detection of brain hemorrhage, covering

the cutting-edge developments of ML in brain

hemorrhage detection and emphasizing the diversity

and depth of ML applications in enhancing diagnostic

accuracy and facilitating timely intervention. The

main contribution is a critical analysis of various

machine learning methods, from traditional machine

learning models to advanced deep learning networks.

This review evaluated their effectiveness, limitations,

and the potential for integration into clinical

workflows, providing insights for future research

directions.

This review is limited to discussing individual

models without fully addressing the potential of

cascading models, which layer processes for

enhanced precision. For instance, a cascading

approach might use CNNs for initial hemorrhage

detection and then apply FCNs for nuanced subtyping

and lesion mapping, offering a path to significantly

refine outcomes. Future updates should delve into

complex models like cascading systems, comparing

their impact on clinical practice, and incorporating

case studies to illustrate real-world applications and

advancements in machine learning for neuroscience.

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