MRI‑Based Brain Tumor Detection and Classification Using Deep

Learning

N. Malarvizhi

, A. Divya

, N. Sankar Ram

, M. Saraswathi

, Naveen Kumar R. J.

and M. Nalini

Professor, Department of CSE, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai,

Tamil Nadu, India

Department of CSE, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

Department of CSE, SCSVMV Deemed to be university, Kanchipuram, Tamil Nadu, India

Senior Associate Engineer, Chennai, Tamil Nadu, India

Associate, Professor, Department of CS&BS, S.A Engineering College, Chennai, Tamil Nadu, India

Keywords: Brain Tumor Detection, MRI Images, Brain Tumor Classification, Fully Convolutional Network (FCN),

Medical Imaging, Automated Diagnosis, Radiology Assistance.

Abstract: It is obvious that the detection and classification of brain tumors in the MRI scans is an important aspect of

medical imaging and so is defected in diagnosis and treatment of medical issues. This work applies a Fully

Convolutional Network (FCN) model towards automatic identification of brain tumors with MRI images from

Kaggle dataset. The dataset is organized into training and testing folders which have subfolders that represent

categories of tumors such as glioma, meningioma, pituitary tumors, and ‘no tumor’ for normal cases. This

lets the FCN learn to not only detect the presence of a tumor, but also which specific type it is. The model is

trained to process MRI images on a pixel-by-pixel basis, allowing for precise segmentation and classification

of abnormal regions. In the event that the model detects a tumor, it will determine the tumor type based on

the set of features that the model has learned for each category in the dataset. The model operates in a sequence

of two stages: first, it classifies an MRI scan to be tumor-positive or tumor-negative; second, if the model

detects the presence of a tumor, it classifies the tumor into one of the set categories. The model performs

binary classification as well as multi-class classification. The proposed system will help to assist radiologists

by providing a tool that is automated and reliable for brain tumor detection and classification, which, without

doubt, simplifies the diagnostics process and improves the outcome.

1 INTRODUCTION

Brain tumors are a life-threatening disease with high

rates of mortality and morbidity. Precise

identification and categorization of brain tumors are

important in determining appropriate therapy

planning and optimal outcomes. The principal

imaging device applied by medical doctors to observe

brain tumors is Magnetic Resonance Imaging (MRI).

With its capacity to produce high-resolution, detailed

images of brain structures, MRI offers significant

information on the existence and nature of tumors.

Yet, such human interpretation is time-consuming

and necessitates specialized knowledge and is even

susceptible to human fallibility or inconsistency

among radiologists. Deep learning algorithms have

been established as strong aids for medical image

analysis automation. Of these, Fully Convolutional

Networks (FCNs) have become increasingly popular

because of their special feature of pixel-level image

segmentation. While conventional Convolutional

Neural Networks (CNNs) classify the entire images,

FCNs are capable of processing images in a manner

that enables fine-grained spatial analysis. This feature

makes them highly appropriate for medical purposes

like the detection and segmentation of brain tumors in

MRI scans. FCNs not only detect if a tumor is present

but also its edges and classify it into certain classes

based on the nature of the tumor. The suggested work

intends to utilize an FCN model to scan brain MRI

images for tumor detection and classification. It

employs a Kaggle dataset, which is an organized

collection of MRI images divided into four classes:

glioma, meningioma, pituitary tumor, and normal (no

tumor). The data structure enables the FCN model to

perform both multi-class classification among tumor

Malarvizhi, N., Divya, A., Ram, N. S., Saraswathi, M., J., N. K. R. and Nalini, M.

MRI-Based Brain Tumor Detection and Classiﬁcation Using Deep Learning.

DOI: 10.5220/0013923200004919

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

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 5, pages

86-91

ISBN: 978-989-758-777-1

and no tumor and multi-class classification among

tumor types. The model achieves this in two steps.

Firstly, the presence of a tumor, and secondly, the

identification of the identified tumor into one of the

specified categories. The incorporation of FCNs in

brain tumor detection systems has numerous benefits.

First, it provides accurate localization of tumor areas,

which is critical for accurate diagnosis and planning

of treatment. Second, it lightens the workload of

radiologists, facilitating quicker and more uniform

decision-making. Third, such automated systems can

fill the gap in health care access, particularly in

locations where trained radiologists are in short

supply. This research sets the effectiveness of FCNs

in classifying and detecting brain tumors as a

potentially scalable and strong solution to medical

imaging. Openly available data and deep learning

methods aid in the progression of automated

diagnostic equipment for brain tumor analysis.

2 LITERATURE SURVEY

A hybrid FCN approach was presented in (Kamran,

A, et, al. 2020) for the classification of brain tumors.

This model leverages the strength of FCNs for

accurate segmentation along with ensemble

classifiers for enhanced classification accuracy. The

work was motivated to address challenges such as

class imbalance and variation in the appearance of the

tumor by combining several classifiers in an

ensemble. A new FCN-based model enriched with

attention mechanisms for brain tumor detection was

suggested by the authors in (Bhatia, R, et, al, 2021)

The research incorporated the pixelwise classification

capability of FCNs and attention layers concentrating

on the area of interest for the tumor. The approach

made the model sensitive to small, abnormally shaped

tumors and better detection accuracy than in

conventional methods.

A multi-scale FCN model for segmentation of

brain tumors from MRI scans was proposed in (Xu,

Y2021). By combining multiple resolution levels in

the segmentation process, the model could preserve

both fine-grained details and large-scale tumor

structures. The multi-scale method enhanced the

accuracy of tumor segmentation, particularly for

tumors with irregular shapes. The authors in (Gupta,

A.,2022) introduced a 3D FCN-based method for

brain tumor detection from volumetric MRI scans.

With the utilization of 3D convolution operations, the

model was able to extract spatial information from

multiple slices of the brain, which enabled more

precise tumor localization and detection in 3D MRI

volumes. This method has proven the great benefits

in utilizing 3D convolutional models compared to 2D

methods in medical image analysis tasks.

A deep supervision FCN model for brain tumor

segmentation was presented in (Wang, Z, 2023) In

this model, more than one layer of the FCN was

supervised directly during training to make sure that

intermediate features were used to contribute to the

final segmentation output. This process enabled the

model to learn better at various abstraction levels and

enhanced its segmentation accuracy, particularly for

heterogeneous tumors. The authors in (Li, X, 2024)

suggested a state-of-the-art multi-class segmentation

FCN for the types of brain tumors (glioma,

meningioma, pituitary tumors, etc.) from MRI scans.

The model aimed to not only segment the tumor area

but also determine the type of tumor, combining both

segmentation and classification into one pipeline. The

authors used a multi-task learning method to train the

model for both tasks at once, which enhanced the

overall performance of both tumor detection and

classification.

The authors proposed a new FCN-enhanced

architecture equipped with spatial attention

mechanisms for the detection of brain tumors in

(Cheng, H, 2024) The attention module was

integrated into the FCN to target areas that were likely

to hold tumor tissue, which aided the model to

perform better at detecting small or inconspicuous

tumors that would be missed using traditional

methods. An FCN-based model using a transfer

learning method for brain tumor segmentation was

presented in (Khan, A, 2020). Taking advantage of

pre-trained models, the research proved enhanced

feature extraction and classification accuracy,

particularly for small datasets. The model achieved

efficient reduction of training time with high

segmentation accuracy.

The authors of (Patel, R,2021) proposed a deep

learning pipeline that combined FCNs with U-Net

architecture for brain tumor segmentation. Their

method utilized skip connections to preserve spatial

information, enhancing segmentation accuracy for

tumors of different shapes and sizes. The paper

emphasized the benefits of employing FCN-based

architectures in medical image tasks. A CNN-FCN

hybrid model for brain tumor detection was

implemented where a Convolutional Neural

Networks (CNNs) used to extract features prior to

inputting them to an FCN for pixel-wise

classification. This approach enhanced small tumor

region detection and minimized false positives in

MRI scans. A CNN-FCN model for brain tumor

segmentation and classification was proposed in

MRI-Based Brain Tumor Detection and Classiﬁcation Using Deep Learning

(Zhang et al, 2023). CNN was used to extract

features, while FCN enhanced tumor localization.

Experimented on the BraTS dataset, the model

reached a Dice score of 0.87, which is superior to

conventional CNN approaches.

3 PROPOSED SYSTEM

In the proposed system, a Fully Convolutional

Network (FCN) is used to automatically Segment

brain tumors in MRI images. Following the

preprocessing step, the FCN processes the Images to

distinguish between tumor regions and healthy tissue.

Trained on a labeled dataset, the model detects and

segments the tumors accurately, helping the

radiologists by accelerating up diagnosis and

improving accuracy. Figure 1 depicts the architecture

diagram of the proposed system.

Figure 1: Architecture diagram for the proposed system.

The various steps in the Proposed System are given

below:

• Data Preprocessing: Resize, normalize, and

augment MRI images from the Kaggle dataset.

• FCN Model Training: Train the Fully

Convolutional Network (FCN) on labeled data

to segment tumor regions.

• Tumor Detection & Segmentation: The

model classifies MRI images into tumor vs. no

tumor and identifies tumor types if present.

• Post-Processing: Generate segmentation

maps and evaluate tumor boundaries.

• Evaluation: Assess model performance using

accuracy, precision, recall, and F1-score.

• Testing & Deployment: Test on unseen data

and deploy the model for real-time clinical

use.

3.1 Input Design

A brain tumor MRI usually contains MRI scan images

classified into different types such as glioma,

meningioma, pituitary tumors, and some non-tumor

cases. Some of these datasets are already available to

the public, such as the Kaggle Brain Tumor MRI

Dataset, the Fig share Brain Tumor Dataset, or the

BraTS (Brain Tumor Segmentation) Dataset, where

labeled images are used in classification and

segmentation tasks. Therefore, these datasets are

widely deployed in deep learning research to advance

the development of automated models for tumor

detection, aiding in early diagnosis and medical

decision support. Figure 2 shows the MRI brain

images used for the proposed system.

Figure 2: MRI brain images.

3.2 Output Design

The output of the proposed system is depicted from

Figure 3 to 6. The results in Figure 3 shows that if a

tumor is present and the type of brain tumor identified

is glioma. It should also contain a suggestion like

“The case should be reviewed by a neurologist.” With

graphical user interfaces, MRI scan images can be

shown along with the results for better context. For

API centrally managed systems, data can easily be

processed through the use of structured JSON. This

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

design is aimed at making things simple to understand

for the users and the medical practitioners as well.

The MRI scan confirms the presence of a

meningioma tumor in Figure 4. It would be best to

check in with a neurologist for assessment and

diagnosis. The findings can be provided in a report or

as an image together with the MRI for clearer

understanding. The results can also be provided via

an API as a JSON for ease of use and processing of

medical information. This solution is best for

healthcare practitioners as well as the patients in

terms of understanding the details.

Figure 3: Found glioma tumor.

Figure 4: Found meningioma tumor.

Figure 5: Found pituitary tumor.

Figure 6: No tumor found.

The model has successfully detected a pituitary

tumor in the MRI scan as shown in Figure 5,

accurately segmenting the affected region and

differentiating it from healthy brain tissue. Using the

trained Fully Convolutional Network (FCN), the

system classifies the tumor based on learned features

with a high confidence score, ensuring reliability in

diagnosis. The segmentation map visually highlights

the tumor boundaries, allowing radiologists to

analyze the size, shape, and location of the

abnormality. This automated detection significantly

reduces diagnosis time, minimizes human error, and

enhances decision-making in treatment planning. The

results can be further validated through clinical

assessment, ensuring that the system provides a

robust and efficient tool for early brain tumor

detection.

The Figure in 6 depicts no tumor found in the MRI

scan. No abnormal growth on the brain or any sign.

However, the patient should continue to seek

attention from a neurologist because the symptoms

can persist. In a structured manner, the findings can

be portrayed as text, or even using a graphical user

interface, accompanying the MRI for confirmation.

With API-based apps, a JSON response can be very

helpful to seamlessly integrate and display results

clearly.

4 RESULTS AND DISCUSSIONS

The proposed Fully Convolutional Network (FCN)

model for brain tumor detection and classification

follows a systematic workflow.

• Data Preparation: The Kaggle dataset is

preprocessed by resizing all MRI images to a

uniform dimension, normalizing pixel values

to enhance contrast, and applying data

augmentation techniques such as rotation,

flipping, and brightness adjustments. This

helps improve model generalization and

prevents overfitting.

MRI-Based Brain Tumor Detection and Classiﬁcation Using Deep Learning

• Model Training: A Fully Convolutional

Network (FCN) is trained in two stages: first,

performing binary classification to distinguish

between tumor and non-tumor cases, and

second, conducting multi-class classification

to identify specific tumor types (glioma,

meningioma, pituitary tumor). The model is

optimized using an Adam optimizer with a

carefully tuned learning rate and trained over

multiple epochs.

• Evaluation: The model’s performance is

evaluated using accuracy, precision, recall,

F1-score, and support as shown in Figure 7 to

assess both classification and segmentation

quality. A confusion matrix is used to analyze

misclassifications, and segmentation results

are visually inspected to validate tumor

localization. Figure 8 presents a consolidated

view of the confusion matrix from Figure 3 to

Figure 6 and showcasing the model's

classification accuracy and segmentation

performance for various tumor types.

• Testing & Deployment: The trained model is

tested on an independent test set to ensure its

robustness in real-world applications. It is

further validated on unseen MRI images to

confirm its ability to accurately detect and

classify tumors. The final model is optimized

for deployment in a clinical setting, where it

can assist radiologists by providing automated

tumor detection and classification results.

Figure 7: Classification report.

Figure 8: Confusion matrix of FCN.

Figure 9: Accuracy and loss.

Figure 6 depicts the training accuracy and training

loss for 50 epochs. As shown in Figure 9 accuracy

increases from 0.35 to 0.60, and loss goes down from

1.45 to 0.85 shown in Figure 9, which is good

learning. The curve of accuracy levels off after 40

epochs, which means a plateau of learning. The

consistent decrease in loss indicates successful

optimization. There could be room for improvement

with more epochs or hyperparameter adjustments.

5 CONCLUSION AND FUTURE

ENHANCEMENT

The proposed FCN-based approach for brain tumor

detection and classification in MRI images, ensuring

accurate segmentation and diagnosis. The proposed

model, with its potential to segment and classify at the

pixel level of an MRI image, ensures high accuracy

in pointing out abnormal zones, hence reducing the

prospect of false diagnosis. As the system runs

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

automatically, the need to intervene is minimized and

also assisting healthcare professionals in making

proper and timely decisions. Future work includes

expanding tumor types, integrating 3D MRI analysis,

improving segmentation with attention mechanisms,

and deploying the model in real-time clinical settings

for practical use.

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MRI-Based Brain Tumor Detection and Classiﬁcation Using Deep Learning