breast-cancer-prediction-ml-python
https://github.com/kaushikjadhav01/breast-cancer-prediction-ml-python
Science Score: 67.0%
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
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✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: scholar.google, zenodo.org -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (10.4%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: kaushikjadhav01
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Size: 11.6 MB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
Breast-cancer-prediction-ML-Python
Early detection of breast cancer seemingly increases a patient's chances of survival and previous studies have shown the successful application of Convolutional Neural Network (CNN) to classify Invasive Ductal Carcinoma (IDC) type cancer from Breast Histopathology images.
In this project, we aim to further enhance the classification performance of CNNs using transfer learning model of VGG16. We also employ techniques like Regularization, Early Stopping, Data Augmentation, Cross Validation and Hyperparameter Optimization during various phases of the project.
Table of Contents
System Description and Functions
Early detection of breast cancer seemingly increases a patient's chances of survival and previous studies have shown the successful application of Convolutional Neural Network (CNN) to classify Invasive Ductal Carcinoma (IDC) type cancer from Breast Histopathology images.
In this project, we aim to further enhance the classification performance of CNNs using transfer learning model of VGG16. We also employ techniques like Regularization, Early Stopping, Data Augmentation, Cross Validation and Hyperparameter Optimization during various phases of the project.
Dataset description
- Sample code number: id number
- Clump Thickness: 1 - 10
- Uniformity of Cell Size: 1 - 10
- Uniformity of Cell Shape: 1 - 10
- Marginal Adhesion: 1 - 10
- Single Epithelial Cell Size: 1 - 10
- Bare Nuclei: 1 - 10
- Bland Chromatin: 1 - 10
- Normal Nucleoli: 1 - 10
- Mitoses: 1 - 10
- Class: (2 for benign, 4 for malignant)
Built With
Installation
- Install Python and Jupyter studio
- Clone repo, cd into it and open the
Proj F IDC Classification.ipynbnotebook in Jupyter.
Authors
Kaushik Jadhav
- Github: https://github.com/kaushikjadhav01
- Medium: https://medium.com/@kaushikjadhav01
- LinkedIn: https://www.linkedin.com/in/kaushikjadhav01/
- Portfolio: http://kajadhav.me/
- Linked In: https://www.linkedin.com/in/kajadhav/
- Dev.to: https://dev.to/kaushikjadhav01
- Codesignal: https://app.codesignal.com/profile/kaushik_j_vtc
- Google Scholar: https://scholar.google.com/citations?user=iRYcFi0AAAAJ
- Daily.dev: https://app.daily.dev/kaushikjadhav01
- Google devs: https://developers.google.com/profile/u/kaushikjadhav01
- Stack Overflow: https://stackoverflow.com/users/21890981/kaushik-jadhav
Links
Owner
- Name: Kaushik Jadhav
- Login: kaushikjadhav01
- Kind: user
- Location: Raleigh, North Carolina, USA
- Company: @microsoft, @ncstate-university, @browserstack
- Website: kajadhav.me
- Repositories: 9
- Profile: https://github.com/kaushikjadhav01
Incoming Cloud & AI SWE Intern @microsoft | MS CS Fall 2022 @ncstate-university | Ex-Software Engineer @browserstack | Applying my engineering skills to solve
Citation (CITATION.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Jadhav" given-names: "Kaushik" orcid: "https://orcid.org/0000-0000-0000-0000" title: "Breast-Cancer-Prediction-ML-Python" version: 2.0.0 doi: 10.5281/zenodo.10499672 date-released: 2023-03-29 url: "https://github.com/kaushikjadhav01/Breast-Cancer-Prediction-ML-Python"