A Convolutional neural network (CNN)model to train and detect skin cancer (benign and malignant) disease using DDI(Diverse Dermatology Images)Dataset.
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Updated
Sep 3, 2023 - Jupyter Notebook
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skin-cancer-classification
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This project uses TensorFlow to implement a Convolutional Neural Network (CNN) for image classification. The goal is to classify skin lesion images into different categories. The dataset used is HAM10000, which contains skin lesion images with associated metadata. The actual accuracy of the model is 90%. 🚀🚀
deep-learning
cnn
image-classification
cnn-classification
ham10000
skin-cancer-detection
skin-cancer-classification
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Updated
May 19, 2024 - Python
Skin Cancer Lesion Detection
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Sep 5, 2023 - Jupyter Notebook
Build a CNN based model which can accurately detect melanoma
data-augmentation
keras-tensorflow
cnn-classification
melanoma-detection
class-imbalance-problem
skin-cancer-classification
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Mar 13, 2024 - Jupyter Notebook
Detection of skin lesions (among 7 classes) using the file https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T and using the pytorch resnet model. The success rate for the specific test file (unseen data) that comes with the download file is 81.13%.
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Mar 18, 2024 - Python
A comprehensive guide on how to implement and train a Compact Convolutional Transformer model that is specifically designed for classifying skin cancer.
computer-vision
neural-network
image-classification
vision-transformer
skin-cancer-classification
compact-convolutional-transformer
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Updated
Mar 16, 2024 - Jupyter Notebook
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