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Commita951f9b

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added skin cancer detection tutorial
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‎README.md

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@@ -45,6 +45,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
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-[How to Use K-Means Clustering for Image Segmentation using OpenCV in Python](https://www.thepythoncode.com/article/kmeans-for-image-segmentation-opencv-python). ([code](machine-learning/kmeans-image-segmentation))
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-[How to Perform YOLO Object Detection using OpenCV and PyTorch in Python](https://www.thepythoncode.com/article/yolo-object-detection-with-opencv-and-pytorch-in-python). ([code](machine-learning/object-detection))
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-[How to Blur Faces in Images using OpenCV in Python](https://www.thepythoncode.com/article/blur-faces-in-images-using-opencv-in-python). ([code](machine-learning/blur-faces))
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-[Skin Cancer Detection using TensorFlow in Python](https://www.thepythoncode.com/article/skin-cancer-detection-using-tensorflow-in-python). ([code](machine-learning/skin-cancer-detection))
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-[Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
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-[How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition))
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-[Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).
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#[Skin Cancer Detection using TensorFlow in Python](https://www.thepythoncode.com/article/skin-cancer-detection-using-tensorflow-in-python)
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To follow along, run:
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-`pip3 install -r requirements.txt`
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Full code is at:
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- Notebook:`skin-cancer-detection.ipynb`
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- Script:`skin-cancer-detection.py`
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importglob
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importos
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importargparse
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importpandasaspd
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defgenerate_csv(folder,labels):
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folder_name=os.path.basename(folder)
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# convert comma separated labels into a list
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label2int= {}
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iflabels:
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labels=labels.split(",")
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forlabelinlabels:
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string_label,integer_label=label.split("=")
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label2int[string_label]=integer_label
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labels=list(label2int)
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# generate CSV file
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df=pd.DataFrame(columns=["filepath","label"])
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i=0
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forlabelinlabels:
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print("Reading",os.path.join(folder,label,"*"))
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forfilepathinglob.glob(os.path.join(folder,label,"*")):
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df.loc[i]= [filepath,label2int[label]]
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i+=1
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df.to_csv(f"{folder_name}.csv")
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if__name__=="__main__":
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parser=argparse.ArgumentParser(description="CSV Metadata generator for skin cancer dataset from ISIC")
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parser.add_argument("-f","--folder",help="Dataset portion folder, e.g: /root/skin-disease/test and not the whole dataset",
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required=True)
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parser.add_argument("-l","--labels",help="The different skin disease classes along with label encoding separated in commas,\
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e.g: Binary classification between malignant and benign categories, something like this:\
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nevus=0,seborrheic_keratosis=0,melanoma=1",
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required=True)
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# parse arguments
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args=parser.parse_args()
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folder=args.folder
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labels=args.labels
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# generate the CSV file
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generate_csv(folder,labels)
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tensorflow
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tensorflow_hub
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matplotlib
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numpy
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pandas
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seaborn
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sklearn
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imblearn

‎machine-learning/skin-cancer-detection/skin-cancer-detection.ipynb

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