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| import tensorflow as tf | |
| import cv2 | |
| import numpy as np | |
| def classify(img): | |
| im = img | |
| lt = ["other","Bone","Brain","eye","kidney","chest","skin"] | |
| im = cv2.resize(im,(52,52)) | |
| model = tf.keras.models.load_model("all-in-one.h5",compile=False) | |
| result = model.predict(np.array([im])) | |
| a = np.argmax(result) | |
| c="" | |
| st.write('________________________________________________') | |
| if a==0: | |
| return "Provide the medical Imaging of the mentioned categories" | |
| st.write('________________________________________________') | |
| if a==1: | |
| c = bone_net(im) | |
| if a==2: | |
| c = brain_net(im) | |
| if a==3: | |
| c = Eye_net(im) | |
| if a==4: | |
| c = kidney_net(im) | |
| if a==5: | |
| c = chest_net(im) | |
| if a==6: | |
| c = skin_net(im) | |
| return c | |
| def bone_net(img): | |
| # img = cv2.resize(img,(224,224)) | |
| lt = ["Normal",'Fractured'] | |
| model = tf.keras.models.load_model("fracture.h5",compile=False) | |
| result = model.predict(np.array([img])) | |
| ans = np.argmax(result) | |
| return lt[ans] | |
| def brain_net(img): | |
| lt = ['pituitary', 'notumor', 'meningioma', 'glioma'] | |
| # img = cv2.resize(img,(52,52)) | |
| model = tf.keras.models.load_model("brain.h5",compile=False) | |
| result = model.predict(np.array([img])) | |
| ans = np.argmax(result) | |
| return lt[ans] | |
| def chest_net(img): | |
| lt = ['PNEUMONIA', 'NORMAL'] | |
| # img = cv2.resize(img,(224,224)) | |
| model = tf.keras.models.load_model("chest.h5",compile=False) | |
| result = model.predict(np.array([img])) | |
| ans = np.argmax(result) | |
| return lt[ans] | |
| def Eye_net(img): | |
| lt = ['glaucoma', 'normal', 'diabetic_retinopathy', 'cataract'] | |
| # img = cv2.resize(img,(224,224)) | |
| model = tf.keras.models.load_model("eye.h5",compile=False) | |
| result = model.predict(np.array([img])) | |
| ans = np.argmax(result) | |
| return lt[ans] | |
| def kidney_net(img): | |
| lt = ['Cyst', 'Tumor', 'Stone', 'Normal'] | |
| # img = cv2.resize(img,(224,224)) | |
| model = tf.keras.models.load_model("kidney.h5",compile=False) | |
| result = model.predict(np.array([img])) | |
| ans = np.argmax(result) | |
| return lt[ans] | |
| def skin_net(img): | |
| lt = ['pigmented benign keratosis', 'melanoma', 'vascular lesion', 'actinic keratosis', 'squamous cell carcinoma', 'basal cell carcinoma', 'seborrheic keratosis', 'dermatofibroma', 'nevus'] | |
| # img = cv2.resize(img,(224,224)) | |
| model = tf.keras.models.load_model("skin.h5",compile=False) | |
| result = model.predict(np.array([img])) | |
| ans = np.argmax(result) | |
| return lt[ans] |