| import numpy as np
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| import pandas as pd
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| import tensorflow as tf
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| import matplotlib.pyplot as plt
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| import seaborn as sb
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| import splitfolders
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| import cv2
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| import random
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| from keras.models import Sequential
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| from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization
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| from tensorflow.keras.utils import plot_model
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| from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, classification_report, accuracy_score
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| from sklearn.utils.class_weight import compute_class_weight
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| from imutils import paths
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| from keras.applications import VGG16
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| from keras.applications import ResNet50
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| from keras.applications import DenseNet121
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| from tensorflow.keras.preprocessing.image import ImageDataGenerator
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| import os
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| from tensorflow.keras.callbacks import ModelCheckpoint
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| import shutil
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|
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|
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| IMGSIZE = 224
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| BATCH_SIZE = 64
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| classes = ['Normal', 'Cyst', 'Tumor', 'Stone']
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| inputFolder = '/content/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone'
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| outputFolder = '/content/CT_dataset'
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| class_to_index = {cls: i for i, cls in enumerate(classes)}
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|
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| splitfolders.ratio(inputFolder, outputFolder, seed=42, ratio=(0.7, 0.15, 0.15))
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| imgPaths = list(paths.list_images(outputFolder))
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| random.shuffle(imgPaths) |
