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

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.

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.

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.

Comparison of a sequential CNN and ResNet50 for brain tumor detection in grayscale MRI scans

Development of Brain Tumor Classification System using Convolutional Neural Network Model with Explainable Artificial Intelligence

A brain tumor classification system integrated with Explainable Artificial Intelligence (XAI) was developed using MRI images and demonstrated effective classification performance and improved interpretability, making it suitable for automated brain tumor diagnosis.

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.