added skin cancer detection tutorial

x4nth055 · x4nth055 · commit a951f9bdb785 · 2020-06-25T09:30:31.000+02:00
diff --git a/README.md b/README.md
@@ -45,6 +45,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [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))
         - [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))
         - [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))
+        - [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))
     - [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))
     - [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))
     - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).
diff --git a/machine-learning/skin-cancer-detection/README.md b/machine-learning/skin-cancer-detection/README.md
@@ -0,0 +1,7 @@
+# [Skin Cancer Detection using TensorFlow in Python](https://www.thepythoncode.com/article/skin-cancer-detection-using-tensorflow-in-python)
+To follow along, run:
+- `pip3 install -r requirements.txt`
+
+Full code is at:
+- Notebook: `skin-cancer-detection.ipynb`
+- Script: `skin-cancer-detection.py`
diff --git a/machine-learning/skin-cancer-detection/generate_metadata.py b/machine-learning/skin-cancer-detection/generate_metadata.py
@@ -0,0 +1,46 @@
+import glob
+import os
+import argparse
+
+import pandas as pd
+
+
+def generate_csv(folder, labels):
+    folder_name = os.path.basename(folder)
+    # convert comma separated labels into a list
+    label2int = {}
+    if labels:
+        labels = labels.split(",")
+        for label in labels:
+            string_label, integer_label = label.split("=")
+            label2int[string_label] = integer_label
+
+    labels = list(label2int)
+    # generate CSV file
+    df = pd.DataFrame(columns=["filepath", "label"])
+    i = 0
+    for label in labels:
+        print("Reading", os.path.join(folder, label, "*"))
+        for filepath in glob.glob(os.path.join(folder, label, "*")):
+            df.loc[i] = [filepath, label2int[label]]
+            i += 1
+
+    df.to_csv(f"{folder_name}.csv")
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="CSV Metadata generator for skin cancer dataset from ISIC")
+    parser.add_argument("-f", "--folder", help="Dataset portion folder, e.g: /root/skin-disease/test and not the whole dataset", 
+                        required=True)
+    parser.add_argument("-l", "--labels", help="The different skin disease classes along with label encoding separated in commas, \
+                        e.g: Binary classification between malignant and benign categories, something like this: \
+                        nevus=0,seborrheic_keratosis=0,melanoma=1",
+                        required=True)
+    # parse arguments
+    args = parser.parse_args()
+    folder = args.folder
+    labels = args.labels
+    # generate the CSV file
+    generate_csv(folder, labels)
+
diff --git a/machine-learning/skin-cancer-detection/requirements.txt b/machine-learning/skin-cancer-detection/requirements.txt
@@ -0,0 +1,8 @@
+tensorflow
+tensorflow_hub
+matplotlib
+numpy
+pandas
+seaborn
+sklearn
+imblearn
diff --git a/machine-learning/skin-cancer-detection/skin-cancer-detection.ipynb b/machine-learning/skin-cancer-detection/skin-cancer-detection.ipynb
diff --git a/machine-learning/skin-cancer-detection/skin-cancer-detection.py b/machine-learning/skin-cancer-detection/skin-cancer-detection.py
