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Browse files- README.md +6 -5
- ada_f1_skin.png +0 -0
- ada_f1_skin_be.png +0 -0
- ada_learn_malben.pkl +3 -0
- ada_learn_skin_norm2000.pkl +3 -0
- akiec1.jpg +0 -0
- app.py +173 -0
- bcc1.jpg +0 -0
- benign1.jpg +0 -0
- benign3.jpg +0 -0
- bkl1.jpg +0 -0
- df1.jpg +0 -0
- gitattributes.txt +29 -0
- mel1.jpg +0 -0
- nevi1.jpg +0 -0
- requirements.txt +2 -0
- scc1.jpg +0 -0
- unk1.jpg +0 -0
- vl1.jpg +0 -0
README.md
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---
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title:
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sdk: gradio
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sdk_version: 3.
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Skin Cancer Diagnose
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emoji: π
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colorTo: indigo
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sdk: gradio
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sdk_version: 3.0.22
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app_file: app.py
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pinned: false
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license: gpl-3.0
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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ada_f1_skin.png
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ada_f1_skin_be.png
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ada_learn_malben.pkl
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version https://git-lfs.github.com/spec/v1
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size 87548073
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ada_learn_skin_norm2000.pkl
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version https://git-lfs.github.com/spec/v1
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akiec1.jpg
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app.py
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import fastai
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import fastai.vision
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import PIL
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import gradio
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import matplotlib
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import numpy
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import pandas
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from fastai.vision.all import *
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#
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# create class
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class ADA_SKIN(object):
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#
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# initialize the object
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def __init__(self, name="Wallaby",verbose=True,*args, **kwargs):
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super(ADA_SKIN, self).__init__(*args, **kwargs)
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self.author = "Duc Haba"
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self.name = name
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if (verbose):
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self._ph()
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self._pp("Hello from class", str(self.__class__) + " Class: " + str(self.__class__.__name__))
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self._pp("Code name", self.name)
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self._pp("Author is", self.author)
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self._ph()
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#
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self.article = '<div><h3>Warning:</h3>Do NOT use this for any medical diagnosis.<br>'
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self.article += 'I am not a dermatologist, and NO dermatologist has endorsed it. '
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self.article += 'This DL model is for my independent research. <br>Please refer to the GPL 3.0 for usage and license.'
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self.article += '<h3>Citation:</h3><ul><li>'
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self.article += 'Author/Dev: Duc Haba, 2022.</li>'
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self.article += '<li><a target="_blank" href="https://linkedin.com/in/duchaba">https://linkedin.com/in/duchaba</a></li>'
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self.article += '<li>The training dataset are from the International Skin Imaging Collaboration (ISIC)</li>'
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self.article += '<li>The Skin Cancer Identification are from 3 seperate datasets.</li>'
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self.article += '<ol>'
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self.article += '<li>https://www.kaggle.com/datasets/surajghuwalewala/ham1000-segmentation-and-classification</li>'
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self.article += '<li>https://www.kaggle.com/datasets/andrewmvd/isic-2019</li>'
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self.article += '<li>https://www.kaggle.com/datasets/jnegrini/skin-lesions-act-keratosis-and-melanoma</li>'
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self.article += '<ul><li>'
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self.article += 'The Malignant versus Benign dataset</li>'
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self.article += '<ol><li>https://www.kaggle.com/datasets/fanconic/skin-cancer-malignant-vs-benign</li>'
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self.article += '</ol></ul>'
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self.article += '<h3>Articles:</h3><ul>'
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self.article += '<li><a target="_blank" href="https://www.linkedin.com/pulse/skin-cancer-diagnose-using-deep-learning-duc-haba/">'
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self.article += '"Skin Cancer Diagnose"</a> on LinkedIn, on <a target="_blank" href='
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self.article += '"https://www.linkedin.com/pulse/skin-cancer-diagnose-using-deep-learning-duc-haba/">Medium.</a></li>'
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self.article += '</ul>'
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self.article += '<h3>Example Images: (left to right)</h3><ol>'
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self.article += '<li>Bowen Disease (AKIEC)</li>'
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self.article += '<li>Basal Cell Carcinoma</li>'
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self.article += '<li>Benign Keratosis-like Lesions</li>'
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self.article += '<li>Dermatofibroma</li>'
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self.article += '<li>Melanoma</li>'
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self.article += '<li>Melanocytic Nevi</li>'
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self.article += '<li>Squamous Cell Carcinoma</li>'
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self.article += '<li>Vascular Lesions</li>'
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self.article += '<li>Benign</li>'
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self.article += '<li>Benign 2</li></ol>'
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self.article += '<h3>Train Result:</h3><ul>'
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self.article += '<li>Skin Cancer Classificaiton: F1-Score, Precision, and Recall Graph</li>'
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self.article += '<li><img src="file/ada_f1_skin.png" alt="F1-Score, Precision, and Recall Graph" width="640"</li>'
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self.article += '<li>Skin Cancer Malignant or Benign: F1-Score, Precision, and Recall Graph</li>'
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self.article += '<li><img src="file/ada_f1_skin_be.png" alt="F1-Score, Precision, and Recall Graph" width="640"</li>'
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self.article += '</ul>'
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self.article += '<h3>Dev Stack:</h3><ul>'
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self.article += '<li>Jupyter Notebook, Python, Pandas, Matplotlib, Sklearn</li>'
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self.article += '<li>Fast.ai, PyTorch</li>'
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self.article += '</ul>'
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self.article += '<h3>Licenses:</h3><ul>'
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self.article += '<li><a target="_blank" href="https://www.gnu.org/licenses/gpl-3.0.txt">GNU GPL 3.0</a></li>'
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self.article += '</ul></div>'
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self.examples = ['akiec1.jpg','bcc1.jpg','bkl1.jpg','df1.jpg','mel1.jpg',
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'nevi1.jpg','scc1.jpg','vl1.jpg','benign1.jpg','benign3.jpg']
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self.title = "Skin Cancer Diagnose"
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return
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#
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# pretty print output name-value line
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def _pp(self, a, b):
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print("%34s : %s" % (str(a), str(b)))
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return
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#
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# pretty print the header or footer lines
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def _ph(self):
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print("-" * 34, ":", "-" * 34)
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return
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#
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def _predict_image(self,img,cat):
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pred,idx,probs = learn.predict(img)
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return dict(zip(cat, map(float,probs)))
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#
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def _predict_image2(self,img,cat):
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pred,idx,probs = learn2.predict(img)
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return dict(zip(cat, map(float,probs)))
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#
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def _draw_pred(self,df_pred, df2):
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canvas, pic = matplotlib.pyplot.subplots(1,2, figsize=(12,6))
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ti = df_pred["vocab"].head(3).values
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ti2 = df2["vocab"].head(2).values
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# special case
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#if (matplotlib.__version__) >= "3.5.2":
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try:
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df_pred["pred"].head(3).plot(ax=pic[0],kind="pie",
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cmap="Set2",labels=ti, explode=(0.02,0,0),
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wedgeprops=dict(width=.4),
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normalize=False)
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df2["pred"].head(2).plot(ax=pic[1],kind="pie",
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colors=["cornflowerblue","darkorange"],labels=ti2, explode=(0.02,0),
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wedgeprops=dict(width=.4),
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normalize=False)
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except:
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df_pred["pred"].head(3).plot(ax=pic[0],kind="pie",
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cmap="Set2",labels=ti, explode=(0.02,0,0),
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wedgeprops=dict(width=.4))
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df2["pred"].head(2).plot(ax=pic[1],kind="pie",
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colors=["cornflowerblue","darkorange"],labels=ti2, explode=(0.02,0),
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wedgeprops=dict(width=.4))
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t = str(ti[0]) + ": " + str(numpy.round(df_pred.head(1).pred.values[0]*100, 2)) + "% Certainty"
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pic[0].set_title(t,fontsize=14.0, fontweight="bold")
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pic[0].axis('off')
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pic[0].legend(ti, loc="lower right",title="Skin Cancers: Top 3")
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#
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k0 = numpy.round(df2.head(1).pred.values[0]*100, 2)
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k1 = numpy.round(df2.tail(1).pred.values[0]*100, 2)
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if (k0 > k1):
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t2 = str(ti2[0]) + ": " + str(k0) + "% Certainty"
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else:
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t2 = str(ti2[1]) + ": " + str(k1) + "% Certainty"
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pic[1].set_title(t2,fontsize=14.0, fontweight="bold")
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pic[1].axis('off')
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pic[1].legend(ti2, loc="lower right",title="Skin Cancers:")
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#
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# # draw circle
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# centre_circle = matplotlib.pyplot.Circle((0, 0), 0.6, fc='white')
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# p = matplotlib.pyplot.gcf()
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# # Adding Circle in Pie chart
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# p.gca().add_artist(centre_circle)
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#
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#p=plt.gcf()
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#p.gca().add_artist(my_circle)
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#
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canvas.tight_layout()
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return canvas
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#
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def predict_donut(self,img):
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d = self._predict_image(img,self.categories)
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df = pandas.DataFrame(d, index=[0])
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df = df.transpose().reset_index()
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df.columns = ["vocab", "pred"]
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df.sort_values("pred", inplace=True,ascending=False, ignore_index=True)
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#
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d2 = self._predict_image2(img,self.categories2)
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df2 = pandas.DataFrame(d2, index=[0])
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df2 = df2.transpose().reset_index()
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df2.columns = ["vocab", "pred"]
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#
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canvas = self._draw_pred(df,df2)
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return canvas
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#
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maxi = ADA_SKIN(verbose=False)
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#
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learn = fastai.learner.load_learner('ada_learn_skin_norm2000.pkl')
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learn2 = fastai.learner.load_learner('ada_learn_malben.pkl')
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maxi.categories = learn.dls.vocab
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maxi.categories2 = learn2.dls.vocab
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hf_image = gradio.inputs.Image(shape=(192, 192))
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hf_label = gradio.outputs.Label()
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intf = gradio.Interface(fn=maxi.predict_donut,
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inputs=hf_image,
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outputs=["plot"],
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examples=maxi.examples,
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title=maxi.title,
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live=True,
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article=maxi.article)
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intf.launch(inline=False,share=True)
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bcc1.jpg
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benign1.jpg
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benign3.jpg
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bkl1.jpg
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df1.jpg
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gitattributes.txt
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bz2 filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.gz filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tgz filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zstandard filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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ada_learn_malben.pkl filter=lfs diff=lfs merge=lfs -text
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ada_learn_skin_norm2000.pkl filter=lfs diff=lfs merge=lfs -text
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mel1.jpg
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nevi1.jpg
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requirements.txt
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fastai>=2.6.3
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pandas>=1.4.1
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scc1.jpg
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unk1.jpg
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vl1.jpg
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