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

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

Comparing Explainable Artificial Intelligence and Deep Learning Models for MRI-Based Brain Tumor Diagnosis

This study investigates the application of deep learning architectures, including Convolutional Neural Network, VGG16, VGG19, ResNet50, and MobileNet, for brain tumor detection and classification and confirms that advanced deep learning architectures not only achieve high classification accuracy but also improve interpretability, thereby offering reliable and clinically applicable solutions for automated brain tumor diagnosis.

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.

Brain Tumor Classification in MRI Images Using Convolutional Neural Networks with Explainable Artificial Intelligence

Brain tumors are a condition requiring rapid and accurate diagnosis. This study aims to enhance the transparency of Convolutional Neural Network (CNN)-based brain tumor classification models by implementing Explainable Artificial Intelligence (XAI) techniques, specifically Eigen-CAM and LIME. The models were developed using a transfer learning approach on ResNet50 and EfficientNetB0 architectures, utilizing a dataset of 3,000 MRI images categorized into glioma, meningioma, and pituitary tumor classes. Test results indicate that ResNet50 achieved the best performance, with accuracy, precision, recall, and F1-score values of 94%, while EfficientNetB0 achieved 93%. The application of 5-fold cross-validation improved the models' generalization capabilities and reduced the risk of overfitting. Visualizations using Eigen-CAM and LIME demonstrate that the models focus on relevant tumor regions, thereby increasing the transparency and reliability of MRI-based classification.

Deep learning-based detection of pediatric brain tumor presence on MRI

The CNN demonstrates strong potential as a diagnostic aid for detection brain tumors using pediatric MRI images, and its integration into clinical environments could contribute to more objective interpretation, optimize diagnostic workflows, and provide imaging follow-up before and after treatment.

Enhanced Brain Tumor Detection and Classification Using Transfer Learning and Explainable AI with Clinical Report Generator

Correctly sorting brain tumors captured through Magnetic Resonance Imaging (MRI) plays a vital role in timely diagnosis and sound clinical decision-making, since a delayed or wrong call can seriously harm patient outcomes. This work introduces AMC-NeuroDx, an upgraded deep-learning pipeline that pits a Custom Convolutional Neural Network (CNN) built from the ground up against a ResNet50 transfer-learning model on a four-way brain tumor classification task (Glioma, Meningioma, Pituitary Tumor, and No Tumor), drawing on 7,223 MRI scans taken from the Kaggle Brain Tumor MRI dataset. ResNet50 follows a two-stage routine—training with a frozen base before full fine-tuning—and reaches 93.16% validation accuracy after only 20 epochs, whereas the Custom CNN needs 50 epochs to hit 90.70%. The framework also embeds Grad-CAM so that spatial heatmaps can show which brain regions drove each prediction, meeting the clinical demand for transparency. On top of this, a Streamlit interface lets users upload MRI scans in real time and automatically produces patient-specific PDF clinical reports holding the diagnosis, confidence scores, Grad-CAM overlays, and recommended next steps. The results obtained here show that transfer learning yields higher accuracy and quicker convergence than building a CNN from the ground up, and together with the built-in explainability and automated report writing, this makes AMC-NeuroDx a practical candidate for real clinical settings.