- Here we have hundreds of images of lungs which are cancerous as well as non-cancerous.
- These images are 1st labelled manually using a csv file provided in which coordinates of tumorms in the image were given and we manullay using those coordinates classified them into cancerous and non-cancerous and then distributed into test and train set.
- Images are then fitted into convolutional neural network to get accuracy and then validation is performed.
- After running the model for about 20+20+5 epochs we got a accuracy of 95.5% with validation accuracy of 85%. Libraries used: from keras.models import Sequential from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import Flatten from keras.layers import Dense import matplotlib.pyplot as plt from PIL import Image from IPython.display import Image from keras.preprocessing.image import ImageDataGenerator import numpy as np from keras.preprocessing import image