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Bone Marrow Cells Classification using Deep Learning #436

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47 changes: 47 additions & 0 deletions Bone Marrow Cells Classification using Deep Learning/README.md
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# <h1 align = "center"> Bone marrow cell classification</h1>
## Aim of the project:
### The project focuses on classification of different types of cells using various Deep Learning Algorithms.

### Libraries and Frameworks used:
1. Pandas
2. Numpy
3. Matplotlib
4. Seaborn
5. Tensorflow
6. Keras
7. sklearn


## Deep Learning Algorithms used:
1. Vgg16
2. EfficientNetB5
3. DenseNet
4. MobileNet

## Accuracy and training time comparison of all the Deep Learning Algorithms
| | Accuracy |
|----------------|---------------|
| Vgg16 | 90% |
| EfficientNetB5 | 92% |
| DenseNet | 81% |
| MobileNet | 86% |

# Representation of types of cells
![EDA](https://github.com/the-silent-geek/DL-Simplified/blob/64d196ea11684a3a2df59b58b912244d9cc8d3fb/Bone%20marrow%20cell%20classification/images/EDA.png)

# Accuracy plots of all models

## Vgg16
![vgg16](https://github.com/the-silent-geek/DL-Simplified/blob/64d196ea11684a3a2df59b58b912244d9cc8d3fb/Bone%20marrow%20cell%20classification/images/VGG16.png)

## EfficientNetB5
![EfficientNetB5](https://github.com/the-silent-geek/DL-Simplified/blob/64d196ea11684a3a2df59b58b912244d9cc8d3fb/Bone%20marrow%20cell%20classification/images/efficient%20net%20b5.png)

## DenseNet
![densenet](https://github.com/the-silent-geek/DL-Simplified/blob/64d196ea11684a3a2df59b58b912244d9cc8d3fb/Bone%20marrow%20cell%20classification/images/DenseNet.png)

## MobileNet
![mobilenet](https://github.com/the-silent-geek/DL-Simplified/blob/64d196ea11684a3a2df59b58b912244d9cc8d3fb/Bone%20marrow%20cell%20classification/images/mobileNet.png)

# Conclusion
EfficientNetB5 model performs better comparative to other models used on the above dataset.
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# Dataset

The dataset used in this project is take from the Kaggle website.
<br>
<b>Dataset Link:- https://www.kaggle.com/datasets/andrewmvd/bone-marrow-cell-classification/data</b>
<br>

<br>
The dataset contains a collection of over 170,000 de-identified, expert-annotated cells from the bone marrow smears of 945 patients stained using the May-Grünwald-Giemsa/Pappenheim stain.
<br>
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1,084 changes: 1,084 additions & 0 deletions Bone Marrow Cells Classification using Deep Learning/model/model.ipynb

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Pandas
Numpy
Matplotlib
Seaborn
Tensorflow
Keras
sklearn