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https://github.com/mpolinowski/tf-bc-classification
Using Tensorflow/Keras for Image Classifications
https://github.com/mpolinowski/tf-bc-classification
image-classification keras python tensorflow2
Last synced: 15 days ago
JSON representation
Using Tensorflow/Keras for Image Classifications
- Host: GitHub
- URL: https://github.com/mpolinowski/tf-bc-classification
- Owner: mpolinowski
- Created: 2022-12-13T05:18:45.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2022-12-16T06:41:25.000Z (about 2 years ago)
- Last Synced: 2024-11-30T11:11:23.327Z (2 months ago)
- Topics: image-classification, keras, python, tensorflow2
- Language: Python
- Homepage: https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/ML/2022-12-10-tf-breast-cancer-classification-part1/2022-12-10
- Size: 159 KB
- Stars: 0
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Breast Histopathology Image Segmentation
Using Tensorflow/Keras for Image Classifications
* [Part 1: Data Inspection and Pre-processing](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/ML/2022-12-10-tf-breast-cancer-classification-part1/2022-12-10)
* [Part 2: Weights, Data Augmentations and Generators](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/ML/2022-12-11-tf-breast-cancer-classification-part2/2022-12-11)
* [Part 3: Model creation based on a pre-trained and a custom model](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/ML/2022-12-11-tf-breast-cancer-classification-part3/2022-12-11)
* [Part 4: Train our model to fit the dataset](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/ML/2022-12-11-tf-breast-cancer-classification-part4/2022-12-11)
* [Part 5: Evaluate the performance of your trained model](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/ML/2022-12-12-tf-breast-cancer-classification-part5/2022-12-12)
* [Part 6: Running Predictions](https://mpolinowski.github.io/docs/IoT-and-Machine-Learning/ML/2022-12-12-tf-breast-cancer-classification-part6/2022-12-12)> Based on [Breast Histopathology Images](https://www.kaggle.com/datasets/paultimothymooney/breast-histopathology-images) by [Paul Mooney](https://www.kaggle.com/paultimothymooney).
> `Invasive Ductal Carcinoma (IDC) is the most common subtype of all breast cancers. To assign an aggressiveness grade to a whole mount sample, pathologists typically focus on the regions which contain the IDC. As a result, one of the common pre-processing steps for automatic aggressiveness grading is to delineate the exact regions of IDC inside of a whole mount slide.`
> [Can recurring breast cancer be spotted with AI tech? - BBC News](https://youtu.be/8XsiMQQ-4mM)* Citation: [Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases](https://pubmed.ncbi.nlm.nih.gov/27563488/)
* Dataset: 198,738 IDC(negative) image patches; 78,786 IDC(positive) image patches