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Title: GitHub - emykes/SkinCancerImageClassification: The aim of this project is to use Convolutional Neural Networks (CNNs) to distinguish dermoscopic images of malignant skin lesions from benign lesions. · GitHub

Open Graph Title: GitHub - emykes/SkinCancerImageClassification: The aim of this project is to use Convolutional Neural Networks (CNNs) to distinguish dermoscopic images of malignant skin lesions from benign lesions.

X Title: GitHub - emykes/SkinCancerImageClassification: The aim of this project is to use Convolutional Neural Networks (CNNs) to distinguish dermoscopic images of malignant skin lesions from benign lesions.

Description: The aim of this project is to use Convolutional Neural Networks (CNNs) to distinguish dermoscopic images of malignant skin lesions from benign lesions. - emykes/SkinCancerImageClassification

Open Graph Description: The aim of this project is to use Convolutional Neural Networks (CNNs) to distinguish dermoscopic images of malignant skin lesions from benign lesions. - emykes/SkinCancerImageClassification

X Description: The aim of this project is to use Convolutional Neural Networks (CNNs) to distinguish dermoscopic images of malignant skin lesions from benign lesions. - emykes/SkinCancerImageClassification

Opengraph URL: https://github.com/emykes/SkinCancerImageClassification

X: @github

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Domain: github.com

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Project_Main_Notebook.ipynbhttps://github.com/emykes/SkinCancerImageClassification/blob/main/Project_Main_Notebook.ipynb
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README.mdhttps://github.com/emykes/SkinCancerImageClassification/blob/main/README.md
READMEhttps://github.com/emykes/SkinCancerImageClassification
https://github.com/emykes/SkinCancerImageClassification#skin-cancer-image-classification
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https://github.com/emykes/SkinCancerImageClassification#author
https://github.com/emykes/SkinCancerImageClassification#project-overview
American Academy of Dermatologyhttps://www.aad.org/media/stats-skin-cancer
Assessment of Raman Spectroscopy for Reducing Unnecessary Biopsies for Melanoma Screeninghttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7355922/
https://github.com/emykes/SkinCancerImageClassification#data-overview
ISIC archivehttps://www.isic-archive.com/#!/topWithHeader/onlyHeaderTop/gallery?filter=%5B%5D
https://github.com/emykes/SkinCancerImageClassification/blob/main/Images/Malignant_Sample.png
https://github.com/emykes/SkinCancerImageClassification/blob/main/Images/Benign_Sample.png
1https://www.kaggle.com/cdeotte/jpeg-melanoma-512x512?select=train.csv
2https://www.kaggle.com/cdeotte/jpeg-isic2019-512x512
3https://www.kaggle.com/c/siim-isic-melanoma-classification/discussion/169139
https://github.com/emykes/SkinCancerImageClassification#methodology
Image_Processing_and_Modelinghttps://github.com/emykes/SkinCancerImageClassification/blob/main/Notebooks/Image_Processing_and_Modeling.ipynb
CNN codealong notebookhttps://github.com/learn-co-curriculum/dsc-convolutional-neural-networks-codealong
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Train faster, generalize better: Stability of stochastic gradient descenthttp://proceedings.mlr.press/v48/hardt16.pdf
https://github.com/emykes/SkinCancerImageClassification#results
https://github.com/emykes/SkinCancerImageClassification/blob/main/Images/confusion_matrix.png
https://github.com/emykes/SkinCancerImageClassification#prediction-explanations
LIME libraryhttps://github.com/marcotcr/lime
https://github.com/emykes/SkinCancerImageClassification/blob/main/Images/benign_correct2.png
https://github.com/emykes/SkinCancerImageClassification/blob/main/Images/malignant_correct2.png
https://github.com/emykes/SkinCancerImageClassification/blob/main/Images/benign_incorrect2.png
https://github.com/emykes/SkinCancerImageClassification/blob/main/Images/malignant_incorrect2.png
https://github.com/emykes/SkinCancerImageClassification#conclusions
Deep Learning Classifier with Patient’s Metadata of Dermoscopic Images in Malignant Melanoma Detectionhttps://www.dovepress.com/deep-learning-classifier-with-patientrsquos-metadata-of-dermoscopic-im-peer-reviewed-fulltext-article-JMDH
https://github.com/emykes/SkinCancerImageClassification#setup-information
Environmenthttps://github.com/emykes/SkinCancerImageClassification/tree/main/Environment
link-1https://www.kaggle.com/cdeotte/jpeg-melanoma-512x512?select=train.csv
link-2https://www.kaggle.com/cdeotte/jpeg-isic2019-512x512
link-3https://www.kaggle.com/c/siim-isic-melanoma-classification/discussion/169139
notebookshttps://github.com/emykes/SkinCancerImageClassification/tree/main/Notebooks
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presentationhttps://github.com/emykes/SkinCancerImageClassification/blob/main/Presentation.pdf
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machine-learning https://github.com/topics/machine-learning
deep-learning https://github.com/topics/deep-learning
image-classification https://github.com/topics/image-classification
convolutional-neural-networks https://github.com/topics/convolutional-neural-networks
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