Multigrade Brain Tumor Classification in MRI Images (Preprint)
Authors: Arora Nakul Sanjeev 
Published: November 2024

BACKGROUND

The diagnosis of brain tumors should be prompt and accurate to ensure an efficient treatment schedule and better results for the patient. MRI is considered the best imaging technique in the diagnosis of brain tumors because it has the advantage of higher resolution imaging. However, there are several disadvantages in interpreting MRI scans, including their manual procedure and potential inconsistencies in readings. Thereby, massive interest in applying deep models based on CNNs springs up for automatic brain tumor classification. However, state-of-the-art models face several challenges of complex spatial interrelations and overfitting capabilities over different datasets. With the work presented here, a new hybrid model called DenseNet201-EfficientNetB0 is proposed to overcome some of these challenges. It means that we use the EfficientNetB0 effectively for feature extraction and the ability of DenseNet201 to extract complex spatial relationships in the MRI images obtained by the brain. Further tuning is done on our suggested model by using transfer learning for three different brain tumor datasets and representing three classes separately: meningioma, glioma, and pituitary tumors. Apparently, our hybrid method yields an appreciable performance with significant superiority over the rest of the competing state-of-the-art techniques, achieving more accurate values for the related metrics of accuracy, precision, and recall concerning the benchmark datasets. Added to this, the lighter structure also gives the opportunity for the model to make computations feasible for real-time applicability in clinical settings. This work offers a strong and scalable solution for brain tumor classification, which opens the door to more reliable diagnostic tools in medical imaging.

OBJECTIVE

Design of Hybrid Model:
This article presents a new hybrid deep learning architecture based on DenseNet-201
and EfficientNetB0. This first contains the part of DenseNet-201 which supports
feature reuse, thus avoiding the vanishing gradients. The latter proposed the network
scaling in order to maximize the performance. Together, these models are able to
extract some critical features from MRI images and complement each other's
strengths in the presentation of a robust architecture of brain tumor classification.
High-Classification Accuracy:
The hybrid model should boost the rates of various accuracy classifications meant for
different types of brain tumors, encompassing meningioma, glioma, and pituitary
tumors. The proposed architecture is trying to boost general classification metrics
such as precision, recall, and accuracy at a number of datasets.
Optimization of the Model's Complexity and Model Efficiency:
Overall, the goal is to achieve the optimal balance between model complexity and
computational efficiency. The hybrid architecture is designed to be relatively
lightweight and efficient, so it could be used in real-time deployment in clinical
environments without compromising on performance.
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Transfer Learning and Fine-Tuning:
Transfer learning techniques are made use of by using already pre-trained
DenseNet-201 and EfficientNetB0 models with the weights from ImageNet, which
then is fine-tuned on the specific brain tumor dataset. The approach thus enables the
adaptation of the models toward idiosyncrasies associated with medical imaging data,
thus making them better at generalization and, therefore, its improvement in
classification accuracy.
Generalization across Multiple Datasets:
The generalization performance of the proposed model is verified through conducting
evaluations on several independent brain tumor datasets. Such results would
guarantee robust performance under different imaging conditions, patient
demographics, and tumor types to demonstrate how adaptable a model would be in
actual applications.
Comparison with State-of-the-Art Models :
This hybrid model would be benchmarked against existing state-of-the-art methods in
the context of brain tumor classification. Any kind of comparison on performance
metrics, such as accuracy, precision, recall, and AUC, would highlight a claim of
proposed architecture to be superior in terms of classifying efficiency and robustness.

METHODS

CNN Transfer Learning using Hybrid model of DenseNet and EfficientNet

RESULTS

High Accuracy of 95%

CONCLUSIONS

The research paper "Multigrade Brain Tumor Classification in MRI Images" mentioned a
hybrid model made up of EfficientNetV2S and Swin Transformers which are used for the
development of brain tumor classification in MRI scans. EfficientNetV2S is the protocol that
efficiently helps in the extraction of features while Swin Transformers are the architecture
that captures spatial relationships across layers, resulting in improved classification accuracy,
precision as well as recall of tumors such as meningioma, glioma, and pituitary tumors.
Particularly, the model is light-weight, thus, it is suitable for real-time clinical applications
and demonstrates strong generalization across diverse datasets. Once its features are properly
inferred it can be re-scaled for multiple medical imaging jobs. To sum up, this model that was
suggested has to be the best, most efficient solution that not only outperforms existing
methods but also is a real tool for improving diagnostic accuracy in healthcare.

CLINICALTRIAL

No