Detecting Brain Tumors Through Multimodal Neural Networks

Antonio Curci

and Andrea Esposito

Department of Computer Science, University of Bari Aldo Moro, Via E. Orabona 4, 70125 Bari, Italy

Keywords:

DenseNet, Brain Tumor, Classiﬁcation, Multimodal Model.

Abstract:

Tumors can manifest in various forms and in different areas of the human body. Brain tumors are speciﬁcally

hard to diagnose and treat because of the complexity of the organ in which they develop. Detecting them

in time can lower the chances of death and facilitate the therapy process for patients. The use of Artiﬁcial

Intelligence (AI) and, more speciﬁcally, deep learning, has the potential to signiﬁcantly reduce costs in terms

of time and resources for the discovery and identiﬁcation of tumors from images obtained through imaging

techniques. This research work aims to assess the performance of a multimodal model for the classiﬁcation

of Magnetic Resonance Imaging (MRI) scans processed as grayscale images. The results are promising, and

in line with similar works, as the model reaches an accuracy of around 99%. We also highlight the need for

explainability and transparency to ensure human control and safety.

1 INTRODUCTION

Brain tumors refer to a heterogeneous group of tu-

mors arising from cells within the Central Nervous

System (CNS) (WHO Classiﬁcation of Tumours Ed-

itorial Board, 2022). These tumors can manifest in

various forms, ranging from benign to malignant, and

may originate within the brain tissue or spread from

other parts of the body through metastasis (Lapointe

et al., 2018). In this regard, it is crucial to underline

that tumors that spread in brains are incredibly com-

plex to treat because of the extreme delicacy that the

organ in question is characterized by.

Brain tumors can rise several symptoms in indi-

viduals who suffer from them, such as strong and

recurring headaches, nausea, altered mental status,

papilledema, and seizures; the implications of these

symptoms in individuals can worsen over time if the

tumor is not detected in time, resulting, eventually, in

death (Alentorn et al., 2016). This implies that the

prompt detection, diagnosis, and removal of tumors

must be supported by proper tools and techniques to

assist professionals and increase their efﬁciency when

performing these tasks. Therefore, there is the need

for ools and instruments featuring the newest tech-

nologies that can support and facilitate this process

for physicians (McFaline-Figueroa and Lee, 2018).

The aid of technology, more speciﬁcally Artiﬁ-

https://orcid.org/0000-0001-6863-872X

https://orcid.org/0000-0002-9536-3087

cial Intelligence (AI), can provide signiﬁcant advan-

tages concerning the precision, speed, and overall ef-

ﬁcacy of detecting these tumors, thereby improving

therapy outcomes and quality of life (Ranjbarzadeh

et al., 2023). In fact, the landscape of AI models for

the detection of brain tumors is vivid (Anaya-Isaza

et al., 2023; Vermeulen et al., 2023; Huang et al.,

2022; Ranjbarzadeh et al., 2023).

Traditionally, brain tumors are diagnosed by us-

ing imaging techniques, such as Magnetic Reso-

nance Imaging (MRI), Computed Tomography (CT),

or Positron Emission Tomography (PET), which are

incredibly useful and effective. However, the in-

tegration of AI in this context can further improve

and enhance their outputs and maximize efﬁciency

(Villanueva-Meyer et al., 2017). Recent research has

focused on using machine learning and deep learning

techniques for brain tumor classiﬁcation, segmenta-

tion, and feature extraction, as well as developing AI

tools to assist neurosurgeons during treatment (Ver-

meulen et al., 2023; Huang et al., 2022).

The current scenario of the application of Neu-

ral Networks employed in the ﬁeld of medicine and

in brain tumor detection encompasses various mod-

els and techniques, and still represents a very chal-

lenging issue. For instance, Mohesen et al. use Deep

Neural Networks (DNN), combined with Principal

Component Analysis (PCA), and Discrete Wavelet

Transform, achieving a good accuracy, around 97%

(Mohsen et al., 2018). Pei et al., instead, used 3D

Curci, A. and Esposito, A.

Detecting Brain Tumors Through Multimodal Neural Networks.

DOI: 10.5220/0012608600003654

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

In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2024), pages 995-1000

ISBN: 978-989-758-684-2; ISSN: 2184-4313

995

Convolutional Neural Networks (CNN), reaching a

training accuracy of around 81% and a validation ac-

curacy of around 75% (Pei et al., 2020). In addition,

Nayak et al. developed another CNN as a variant of

Efﬁcient DenseNets with dense and drop-out layers,

obtaining an accuracy close to 99% (Nayak et al.,

2022). The employment of these models in classiﬁ-

cation tasks in medicine can be signiﬁcantly useful.

At the same time, it remains crucial for profession-

als to maintain control and be able to check the out-

put of these instruments to have the ﬁnal say over

the model’s predictions. The employment of multi-

modal models, instead, is still under development and

research in the literature. It is possible to ﬁnd cases in

which these models are built with Multi-Layer Per-

ceptrons (MLP) or with DenseNets for 3D images

image classiﬁcation, in which researchers could not

achieve high-performance rates (Ma and Jia, 2020;

Latif et al., 2017). Different modalities provide dif-

ferent types of information. Images can visual in-

formation about the tumor’s location, size, and ex-

ternal characteristics, while tabular data can include

insights about other aspects and peculiarities either

highlighted by the physician or numerical data ex-

tracted from the images themselves. Combining these

modalities can improve the AI model when it comes

to learning how to discriminate between tumor and

non-tumor cases. Multimodal AI can also provide

a more comprehensive decision support system for

healthcare professionals, leading to better clinical

decision-making and treatment planning (Soenksen

et al., 2022; Yang et al., 2016).

This research work aims at creating and employ-

ing a multi-modal model to classify brain images as

healthy or ill (i.e., containing a tumor) and propos-

ing an approach towards stronger explainability and

transparency to increase physicians’ trust levels when

using AI in medicine. The model in question was built

through a Densely Connected Convolutional Network

(DenseNet) and it was trained over a labeled dataset

composed of tabular data and 2D brain tumor images.

This paper is organized as follows: section 2 en-

compasses all the materials used during this study,

deﬁning the dataset, its provenance, and the distribu-

tion of the classes; section 3 explores the model, its

structure, and the parameters set for the experiment.

In section 4, we describe the tools used to carry out

the experiment and we analyze the results; section 5,

instead, provides an overview of the research work,

its outcomes, and the future directions that we intend

to undertake for this project, highlighting the need for

explainability and control.

2 MATERIALS

This research work was conducted using a dataset de-

rived from the BRATS 2015 challenge (Menze et al.,

2015), freely available on Kaggle.com (Jakesh Bo-

haju, ). The dataset comprises 3762 instances. Each

instance consists in a 240 × 240 three-channel MRI

scans of the brain, and in a set of 13 numeric features

(with an additional feature that allows to identify the

scan associated with the numeric values). The dataset

is fully labeled. The labels are binary and mutually

exclusive: a value of “0” represents the absence of

a tumor (in the following, we will refer to this class

as “healthy”); a value of “1” indicates the presence

of a tumor (in the following, we will refer to this

class as “ill”). The tabular data has 13 features of

ﬁrst- an second-order; they were extracted by the au-

thors of the dataset from the images, which are the

processed output of MRI scans. The ﬁrst-order fea-

tures are Mean, Variance,Standard Deviation, Skew-

ness, Kurtosis, while those of second-order are En-

tropy, Contrast, Entropy, Energy, Dissimilarity, Cor-

relation, Coarseness, ASM (Angular second moment),

Homogeneity.

The dataset is slightly unbalanced, with 2079 in-

stances labeled as healthy and 1683 labeled as ill.

To avoid the potential introduction of artifacts or

unrealistic samples using data augmentation (Chlap

et al., 2021), the class-imbalance problem was solved

by dropping randomly selected instances from the

“healthy” class. The numeric features of the dataset

(a) Healthy scan.

(b) Scan presenting a tu-

mor.

the ill brain.

Figure 1: Examples of MRI scans available in the dataset.

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Figure 2: Architecture of the multi-modal deep neural network.

have been standardized in order to have mean µ = 0

and variance σ

= 1. The dataset has no missing val-

ues, making it unnecessary to perform any additional

pre-processing.

Figure 1 provides examples of images labeled as

healthy and ill. More speciﬁcally, Figure 1b is the im-

age of the scan of a brain containing a tumor, which is

found in its lower-right part as a white area that stands

out from the rest of the organ; the latter is pointed in

Figure 1c in the highlighted red rectangle.

3 METHODS

The model used for this research work is a multi-

modal neural network. The model architecture, de-

picted in Figure 2, is composed of two heads (one

for each type of input data). The ﬁrst head is respon-

sible for the feature extraction from the MRI scans:

it consists in a DenseNet121 network (Huang et al.,

2018) with input size 240 × 240 × 3 and output size

7 × 7 ×1024, that is then ﬂattened. The second head,

responsible for the encoding of the tabular data, con-

sists in a simple fully-connected neural network, us-

ing the Rectivied Linear Unit (ReLU) activation func-

tion. The outputs of the two heads are then concate-

nated and normalized. The resulting vector is then

provided as input to an additional fully-connected

neural network (also using the ReLu activation func-

tion), which terminates in two SoftMax-activated neu-

rons that provide the ﬁnal prediction. The model is

shown in Figure 2.

4 RESULTS

The experiment was performed using an Apple Sil-

icon M2 Pro chip with an integrated 16-core GPU,

using the TensorFlow library.

To evaluate the proposed method, a stratiﬁed 10-

fold cross-validation was used (i.e., each fold con-

tained roughly the same proportion of the two class

labels). For the training phase, we used binary cross-

entropy as the loss function, deﬁned in Equation 1,

where y

is the ground truth label, while p

is the

model output for an individual observation.

H (y, p) =

∑

i=1

−(y

log(p

) + (1 − y

)log(1 − p

))

(1)

Cross-entropy was minimized using the Adam op-

timizer, with a static learning rate of 10

−3

and a batch

size of 32. The maximum number of epochs was set

to 10

, with an early stopping criterion based on the

Detecting Brain Tumors Through Multimodal Neural Networks

997

Table 1: Results of the cross validation.

CV Fold Accuracy AUC Loss Precision Recall F1-Score

1 0.99 0.99 0.18 0.99 0.98 0.99

2 0.97 0.97 1.5 0.99 0.95 0.97

3 0.99 0.99 5.6e-05 0.99 0.99 0.99

4 0.98 0.98 1.3 0.98 0.98 0.98

5 0.97 0.98 0.67 0.95 0.99 0.97

6 0.99 0.99 0.84 0.99 0.99 0.99

7 0.99 0.99 0.72 0.99 0.99 0.99

8 0.98 0.98 2.9 0.99 0.96 0.98

9 0.99 0.99 0.22 0.99 0.99 0.99

10 0.99 0.99 0 0.99 0.99 0.99

Avg. 0.99 0.99 0.83 0.99 0.98 0.98

validation loss with a minimum delta of 10

−4

and a

patience of 5 epochs.

As performance metrics, we opted for the most

commonly used metric in classiﬁcation problems:

• Accuracy: deﬁned as the proportion of the cor-

rectly classiﬁed samples (both positives and neg-

atives) in the selected population.

• Recall: which refers to the proportion of diseased

subjects who have been classiﬁed as ill;

• Precision: that is the proportion of the correctly

classiﬁed samples among all ill-classiﬁed sam-

ples;

• F1-Score: that is the harmonic mean between the

precision and recall;

• Area Under ROC-Curve (AUC): that indicates the

probability that, given a healthy and an ill sample,

the classiﬁer is able to correctly distinguish them.

The training phase on the 10 folds exhibited quite

good performances, shown in Table 1; each fold gen-

erated accuracy rates higher than 97%, with an av-

erage of 98.80%. The average values for all metrics

Figure 3: Results of the cross-validation.

are available in Table 1. The loss has values less than

1.5, as shown in Figure 2. The only exception is the

eighth fold, which has a loss value close to 2.9: fur-

ther inspection is needed to uncover the reasons for

this sudden peak.

5 CONCLUSIONS AND FUTURE

WORKS

In this article, we explore the use of multi-modal

DenseNets for brain tumor images classiﬁcation. The

presented model is useful when dealing with data of

different types with intrinsically different representa-

tions, in this case, tabular data and images. The multi-

modal Deep Neural Network created and exploited in

this case study provides promising results for classi-

fying brain tumor images, achieving an average ac-

curacy of 98%. The results are on par with other

techniques found in the literature (Nayak et al., 2022;

Mohsen et al., 2018).

Although the dataset used in this work was also

used by other researchers in the community, a multi-

modal model was never chosen as the approach to un-

dertake to perform a classiﬁcation task. Arora et al

created a model consisting of a Convolutional Neural

Network (CNN) that reached a 90% accuracy using

a VGG16 Neural Network (Arora and Sharma, 2021;

Simonyan and Zisserman, 2015). Herm et al, instead,

employed a CNN with 13 layers, obtaining an accu-

racy of around 89% (Herm et al., 2023). Another re-

search work was performed on this dataset by Morris

L., whose model achieved 87% of accuracy by using a

Deep Neural Network with MobileNetV2 (Lee, 2022;

Sandler et al., 2019). It emerges that the work pre-

sented in this article provides a starting ground for fu-

ture research, exploring how the exploitation of differ-

ent types of data can be for the classiﬁcation of brain

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tumor images.

An initial aspect that needs further exploring is the

model generalizability: future work may delve in test-

ing the model with additional parameters and/or an-

other dataset with the same structure and belonging

to the same medical domain, to observe its behavior

and efﬁcacy in different settings. Moreover, future

work involves also the comparison of the performance

of this multi-modal model with standard classiﬁers,

meaning models that are trained merely on tabular

data or on images. The objective is to determine how

the characteristics of the model proposed in this work

can be beneﬁcial to the medical ﬁeld with respect to a

more traditional approach.

In addition, explainability and transparency are

needed to provide users (i.e., physicians) with more

efﬁcient instruments to understand and comprehend

the outputs it provides. As neural networks’ out-

puts are usually obscure to users without expertise in

computer science and, speciﬁcally, in AI, explainabil-

ity has the potential of demystifying the process that

lies behind the ﬁnal predictions and output of mod-

els. Moreover, it is crucial for physicians to fully

understand the reasons why an AI systems provided

a speciﬁc outcome (Combi et al., 2022), as this en-

sure human control. In fact, from an ethical point

of view, the responsibility that physicians undertake

when making decisions about the health state of their

patients cannot depend merely on algorithms that they

do not comprehend properly. Explainability plays

an important role for physicians because it allows to

check and keep track of which features were relevant

for the prediction outputted by the AI model and de-

tecting potential mistakes that can be corrected thanks

to their expertise. The motivation behind this lies in

the fact that AI systems are never perfectly accurate,

thus, the clinical revision process has to be carried

out precisely and meticulously by professionals, im-

plying that having complete and blind trust is not fea-

sible for legal reasons, too (Amann et al., 2020). It

emerges that the goal is to approach a symbiotic re-

lationship between AI and humans. The use of AI in

medicine, especially Neural Networks, can be beneﬁ-

cial both diagnostically and to foster and guide future

research (e.g., through machine teaching (Selvaraju

et al., 2016)).

The multi-modal neural network presented in this

article provides an interesting proving ground, to

evaluate the balance between accuracy, model com-

plexity, and explainability in a challenging high-risk

domain.

ACKNOWLEDGEMENTS

The research of Antonio Curci is supported by the

co-funding of the European Union - Next Genera-

tion EU: NRRP Initiative, Mission 4, Component 2,

Investment 1.3 – Partnerships extended to universi-

ties, research centers, companies, and research D.D.

MUR n. 341 del 15.03.2022 – Next Generation

EU (PE0000013 – “Future Artiﬁcial Intelligence Re-

search – FAIR” - CUP: H97G22000210007).

The research of Andrea Esposito is funded by a

Ph.D. fellowship within the framework of the Italian

“D.M. n. 352, April 9, 2022” - under the National Re-

covery and Resilience Plan, Mission 4, Component 2,

Investment 3.3 - Ph.D. Project “Human-Centered Ar-

tiﬁcial Intelligence (HCAI) techniques for supporting

end users interacting with AI systems”, co-supported

by “Eusoft S.r.l.” (CUP H91I22000410007).

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