Helping detect the type of brain tumor (if any) using EfficientNetB1.
-
Updated
Aug 8, 2021 - Jupyter Notebook
#
efficientnetb1
Here are 10 public repositories matching this topic...
Explore deep learning-powered image classification with PyTorch. Achieved 98% accuracy on Natural Images and 95% on Birds Species using AlexNet and EfficientNet-B1. Dive into the code and results!
deep-learning pytorch image-classification alexnet vgg16 natural-images bird-species-classification efficientnetb1
-
Updated
Aug 19, 2023 - Jupyter Notebook
Finetuned pretrained-CNN architectures on Kaggle's FGVC21 plant pathology dataset (Implemented in Keras)
kaggle-competition cnn-keras kaggle-dataset multi-class-classification cnn-classification inception-resnet-v2 cnn-image-classification efficientnetb1
-
Updated
Dec 13, 2021 - Jupyter Notebook
In this project, we created a convolutional neural network using the EfficientNetB1 model in Keras to perform Image Classification of MRI brain scans with reasonably high (97.4%) accuracy.
tumor mri brain convolutional-neural-network classification-model glioma efficientnetb1 pituitary meningioma
-
Updated
Mar 15, 2022 - Jupyter Notebook
Skin lesion classification using EfficientNetB1 with transfer learning on the HAM10000 dataset. The project addresses class imbalance with weighted loss and data augmentation, achieving improved accuracy and robustness compared to previous CNN-based models.
tensorflow keras cnn confusion-matrix data-augmentation keras-tensorflow cnn-classification skin-lesion-classification ham10000 skin-cancer-detection efficientnetb1 skin-cancer-detection-cnn onvolutional-neural-networks
-
Updated
Dec 27, 2025 - Jupyter Notebook
Plant Disease Classifier using a fine-tuned EfficientNetB1 model (96.68% accuracy on Kaggle). Upload a leaf image, use your webcam, or paste from the clipboard to detect the plant species and disease with confidence scores.
-
Updated
Jun 12, 2025 - Python
A multi-modal deep learning pipeline for skin lesion classification on HAM10000 that fuses dermoscopic images and clinical metadata using an ImageNet-pretrained EfficientNetB1 backbone, addressing class imbalance via class weights and achieving strong validation performance.
computer-vision tensorflow keras medical-imaging transfer-learning class-imbalance skin-lesion-classification multi-modal-learning ham10000 skin-cancer-detection efficientnetb1 clinical-metadata-fusion
-
Updated
Jan 6, 2026 - Jupyter Notebook
A high-resolution (384×384) multi-modal deep learning framework for skin lesion classification on the HAM10000 dataset, combining dermoscopic images and clinical metadata using EfficientNetB1 and a two-stage fine-tuning strategy to improve minority-class performance.
deep-learning convolutional-neural-networks transfer-learning medical-image-analysis skin-lesion-classification multi-modal-learning ham10000 efficientnetb1 high-resolution-images clinical-metadata
-
Updated
Jan 3, 2026 - Jupyter Notebook
Projet de détection de la COVID-19 grâce aux réseaux de neurones convolutifs.
deep-learning tensorflow keras cnn convolutional-neural-networks vgg16 cnn-classification streamlit covid-19 efficientnetb1
-
Updated
Oct 26, 2023 - Jupyter Notebook
Skin lesion classification on the HAM10000 dataset using EfficientNetB1 with transfer learning and fine-tuning. The model applies data augmentation, class weighting, and staged unfreezing to handle class imbalance and improve validation performance across 7 lesion categories.
computer-vision deep-learning tensorflow keras cnn medical-imaging transfer-learning keras-neural-networks keras-tensorflow fine-tuning skin-lesion-classification ham10000 efficientnetb1
-
Updated
Jan 1, 2026 - Jupyter Notebook
Improve this page
Add a description, image, and links to the efficientnetb1 topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the efficientnetb1 topic, visit your repo's landing page and select "manage topics."