EFFICIENTNET-BASED MULTI-CLASS BRAIN TUMOR CLASSIFICATION FROM MRI IMAGES: A ROBUST DEEP LEARNING FRAMEWORK

Multi-Class Classification of Brain Cancer Based on MRI Images Using MobileViT Architecture

Brain cancer is a disease with serious impacts on neurological functions and patients’ quality of life. Early detection via MRI is crucial, yet manual image interpretation by radiologists is subjective and prone to errors. While deep learning approaches offer automation solutions, conventional models are often constrained by high computational burdens, making them difficult to implement on resource-constrained devices. Addressing these challenges, this study develops an efficient multi-class classification system using the Mobile Vision Transformer (MobileViT) architecture to detect four conditions: glioma, meningioma, pituitary, and normal within MRI datasets. The research methodology encompasses three systematic experimental scenarios covering hyperparameter optimization via grid search, comparative analysis of fine-tuning strategies, and evaluation of the impact of geometric data augmentation on model generalization. Performance evaluation is conducted based on accuracy, precision, recall, and F1-score metrics. The results indicate that the configuration of the AdamW optimizer, a learning rate of 0.001, and a dropout of 0.2 provides optimal training stability. Significant findings reveal that the fine-tuning strategy is crucial for medical feature adaptation, achieving an accuracy surge to 97.40%. Furthermore, the application of data augmentation successfully reduced prediction bias and strengthened the model’s robustness against input variations, increasing the accuracy to 98.70%. These results demonstrate that MobileViT is capable of delivering superior diagnostic performance while maintaining computational efficiency.

BrainTumor CNN: brain tumor classification via transfer learning and ResNet-18

This study proposes BrainTumor CNN, a convolutional neural network (CNN) for classifying brain tumor MRI images, which leverages transfer learning via a pre-trained ResNet-18 network, integrating data augmentation and Dropout regularization to enhance robustness and generalization.

Benchmarking Deep Convolutional Neural Networks for Brain Tumor Detection Using Magnetic Resonance Imaging Data

Accurate Brain Tumor Classification Using MRI Images Based on A Hybrid Vision Transformer and BiLSTM Framework

Results show that ViT–BiLSTM's classification performance is superior to those of traditional deep learning methods: among all the tumor categories its accuracy is higher, its fine-tuning more perfect, as well as, its Recall rates greater.

A Novel Explainable Deep Learning Model for Early-Stage Brain Tumor Classification: Multi-Level Feature Fusion from Merged MRI Datasets

A unique explainable deep learning model that integrates Tiny-ConvNeXt and DenseNet169 using multi-stage brain tumor classification is proposed, indicating that the suggested model provides a transparent and reliable framework for brain tumor detection, with potential applications in practical clinical decision support systems.

Brain Tumor Detection Using MRI Images

The study adds a rigorous benchmarking mechanism and empirical evidence for adopting ResNet50 as a robust model for multi-class brain tumour diagnosis and highlights the power of deep residual learning for solving some of the difficulties in classifying brain MRI, such as inter-class similarity and feature heterogeneity.