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import tensorflow as tf
import cv2
import numpy as np
cls_model = tf.keras.models.load_model("all-in-one.h5",compile=False)
fract_model = tf.keras.models.load_model("fracture.h5",compile=False)
brain_model = tf.keras.models.load_model("brain.h5",compile=False)
chest_model = tf.keras.models.load_model("chest.h5",compile=False)
eye_model = tf.keras.models.load_model("eye.h5",compile=False)
kid_model = tf.keras.models.load_model("kidney.h5",compile=False)
skin_model = tf.keras.models.load_model("skin.h5",compile=False)
def classify(img):
im = img
lt = ["other","Bone","Brain","eye","kidney","chest","skin"]
im = cv2.resize(im,(52,52))
result = cls_model.predict(np.array([im]))
a = np.argmax(result)
c=""
if a==0:
return "Enter the medical Image"
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 = ['not fractured', 'fractured']
result = fract_model.predict(np.array([img]))
# 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))
result = brain_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))
result = chest_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))
result = eye_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))
result = kid_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))
result = skin_model.predict(np.array([img]))
ans = np.argmax(result)
return lt[ans] |