import tensorflow as tf

from tensorflow.keras import models, layers

import matplotlib.pyplot as plt

from pprint import pprint

import cv2

import numpy as np​

Load Dataset :

import pathlib

from pathlib import Path

my_path = Path(r'/content/Skin disease/Train ')



skin_images_dict = {

    'Chickenpox': list(my_path.glob('Chickenpox /*')),

    'Impetigo': list(my_path.glob('Impetigo /*')),

    'Infectious erythema': list(my_path.glob('Infectious erythema /*')),

    'Scabies': list(my_path.glob('Scabies /*')),

    'Skin warts': list(my_path.glob('Skin warts /*'))

}

skin_labels_dict = {

    'Chickenpox': 0,

    'Impetigo': 1,

    'Infectious erythema': 2,

    'Scabies': 3,

    'Skin warts':4

}

Dependent and Independent Variable:

X = []

y = []

for tumor_name,images in skin_images_dict.items():

    for image in images:

        img = cv2.imread(str(image))

        resize = cv2.resize(img,(200,200))

        X.append(resize)

        y.append(skin_labels_dict[tumor_name])

Perform some data preprocessing :

X = np.array(X)

y = np.array(y)



X_scaled = X/255

Model Creation :

from tensorflow.keras import  models,layers

model = models.Sequential([

    layers.Conv2D(filters=32, kernel_size=(4, 4),

                  activation='relu', input_shape=(200, 200, 3)),

    layers.MaxPooling2D(pool_size=(2, 2)),

    

    layers.Conv2D(filters=64, kernel_size=(3, 3),

                  activation='relu'),

    layers.MaxPooling2D(pool_size=(2, 2)),

    layers.Conv2D(filters=124, kernel_size=(3, 3),

                  activation='relu'),

    layers.Dropout(0.5),

    layers.Flatten(),

    layers.Dense(270,activation='relu'),

    layers.Dense(5,activation='softmax')

]

)

model.compile(

    optimizer='adam',

    loss='sparse_categorical_crossentropy',

    metrics=['accuracy']

)

Train Model :

model.fit(X_scaled,y,epochs=20)

Model Evaluation :
Load Test data :

test_path = Path(r'/content/Skin disease/Test ')

test_images_dict = {

    'Chickenpox': list(test_path.glob('Chickenpox/*')),

    'Impetigo': list(test_path.glob('Impetigo/*')),

    'Infectious erythema': list(test_path.glob('Infectious erythema/*')),

    'Scabies': list(test_path.glob('Scabies/*')),

    'Skin warts': list(test_path.glob('Skin warts/*'))

}

Perform data processing operations on test data as we perform on train data.

X_test = []

y_test = []



for tumor_name,images in skin_images_dict.items():

    for image in images:

        img = cv2.imread(str(image))

        resize = cv2.resize(img,(200,200))

        X_test.append(resize)

        y_test.append(skin_labels_dict[tumor_name])

X_test = np.array(X_test)

y_test = np.array(y_test)



X_test_scaled = X_test/255

Check Accuracy for test data :

model.evaluate(X_test_scaled,y_test)

Classification Report :
Class 0 : Chickenpox
Class 1 : Impetigo
Class 2 : Infectious erythema
Class 3 : Scabies
Class 4 : Skin warts

y_pred = list()

for i in model.predict(X_test_scaled):

  y_pred.append(np.argmax(i))

y_pred = np.array(y_pred)



from sklearn.metrics import  classification_report,confusion_matrix

print(classification_report(y_test,y_pred))

Confusion Matrix :

import seaborn as sns

import pandas as pd

class_names = [0,1,2,3,4]

fig,ax = plt.subplots()

tick_marks = np.arange(len(class_names))

plt.xticks(tick_marks,class_names)

plt.yticks(tick_marks,class_names)

cnf_matrix = confusion_matrix(y_test,y_pred)

sns.heatmap(pd.DataFrame(cnf_matrix), annot = True, cmap = 'YlGnBu',

                fmt = 'g')

ax.xaxis.set_label_position('top')

plt.tight_layout()

plt.ylabel('Actual label')

plt.xlabel('Predicted label')

plt.show()

Thank You ):