RadImageNet, a pre-trained convolutional neural networks trained solely from medical imaging to be used as the basis of transfer learning for medical imaging applications.

Unofficial support to RadImageNet pretrained models for Pytorch

An advanced deep learning tool for Alzheimer's disease diagnosis using a CNN with transfer learning from DenseNet-121 pre-trained on RadImageNet, achieving a test accuracy of 95.13%. It features a user-friendly interface for uploading MRI scans and provides immediate classification into AD, MCI, or CN stages.

RadImageNet in PyTorch. RadImageNet is an open-access medical imaging database designed for deep learning research and effective transfer learning.

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Brain Tumor Detection using Pretrained RadImageNet ResNet50 with SmoothGrad Visualization

Deep learning framework for automated tooth detection, enumeration, and disease classification in dental radiographs using YOLO and RadImageNet.

Classification networks pre-trained on ImageNet and RadImageNet for expainable brain tumor detection on the Cheng get al. dataset

This repository contains the implementation of our minor project "Deep Learning and Nature-Inspired Algorithms for Alzheimer’s Diagnosis: A Computer Vision Approach", submitted as part of the Bachelor of Technology program in Computer Science and Engineering at Maharaja Agrasen Institute of Technology, New Delhi (affiliated with GGSIPU).

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Refined RadImagenet and Benchmarks

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Updated version of tarun3991/RadImageNet

Pretrained RadImageNet ResNet 50 with Adaptive NN PPO

Analysis of pre-trained CNNs (VGG16, ResNet18, MobileNetV2, and a RadImageNet-pretrained ResNet50) for malignant vs benign classification on the VinDr-Mammo dataset, exploring transfer-learning viability under limited-resource constraints.

Official repository for 'General vs Domain-Specific CNNs: Understanding Pretraining Effects on Brain MRI Tumor Classification'. Contains code, models, and experiments comparing RadImageNet DenseNet121, EfficientNetV2S, and ConvNeXt-Tiny on brain tumor classification.

A comparative study of ImageNet and RadImageNet pretraining for endoscopic image segmentation using ResNet-50 and ViT-Small backbones. This work evaluates the impact of pretraining domain and modality alignment on segmentation performance across three public polyp datasets.

The project uses advanced deep learning and medical image processing to diagnose Alzheimer's disease, classifying MRI scans into Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), or Cognitively Normal (CN). Powered by DenseNet-121 with RadImageNet pretraining, it offers accurate, early detection via user-friendly interface for clinical use