A Hybrid Deep Learning-Based Approach for Brain Tumor Classification

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Full frame distilled prediction

Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

Candidate categories

none

Consensus categories

none

Domain

Candidate signal: noneConsensus signal: none

Study design

Candidate signal: Bench or experimentalConsensus signal: none

Genre

Candidate signal: EmpiricalConsensus signal: none

Teacher disagreement score

0.891

Threshold uncertainty score

0.671

Validation status

machine_predicted_unvalidated · codex-gemma-dda1882f352a

Codex and Gemma teacher scores by category

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Teacher spread

0.229 · how far apart the two teachers sit on this one work

Validation status

score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images.

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The record

Venue

Electronics

Topic

Brain Tumor Detection and Classification

Field

Neuroscience

Canadian institutions

Université de Moncton

Funders

not available

Keywords

Convolutional neural networkArtificial intelligenceDeep learningComputer scienceF1 scoreFeature extractionPattern recognition (psychology)Feature (linguistics)Machine learningArtificial neural network

Has abstract in OpenAlex

yes

API

/api/v1/works/W4226313662