giulianevesmonteiro/Pneumonia-Detection-Using-CNN

Used Convolutional Neural Networks (CNNs), an advanced machine learning technique, to develop predictive models that accurately diagnoses if a patient has pneumonia based on their X-rays. This project was done analyzing pneumonia cases in children under 5 years old in Uganda. # Pneumonia-Detection-Using-CNN Used Convolutional Neural Networks (CNNs), an advanced machine learning technique, to develop predictive models that accurately diagnoses if a patient has pneumonia based on their X-rays. This project was done analyzing pneumonia cases in children under 5 years old in Uganda. #import os #os.system ("pip install keras==2.4.3") !pip install tensorflow import keras print('The keras version is {}.'.format(keras.__version__)) # IPython display functions import IPython from IPython.display import display, HTML, SVG, Image # General Plotting import matplotlib.pyplot as plt plt.style.use('seaborn-paper') plt.rcParams['figure.figsize'] = [10, 6] ## plot size plt.rcParams['axes.linewidth'] = 2.0 #set the value globally ## notebook style and settings display(HTML(" .container { width:90% !important; } ")) display(HTML(" .output_png { display: table-cell; text-align: center; vertical-align: middle; } ")) display(HTML(" .MathJax {font-size: 100%;} ")) # For changing background color def set_background(color): script = ( "var cell = this.closest('.code_cell');" "var editor = cell.querySelector('.input_area');" "editor.style.background='{}';" "this.parentNode.removeChild(this)" ).format(color) display(HTML(' '.format(script))) Library Imports import os import sys import random import numpy as np import pandas as pd from os import walk # Metrics from sklearn.metrics import * # Keras library for deep learning # keras.io import tensorflow as tf import keras from keras.datasets import mnist # MNIST Data set from keras.models import Sequential # Model building from keras.layers import * # Model layers from keras.preprocessing.image import * from tensorflow.keras.utils import * from sklearn.model_selection import train_test_split import warnings warnings.simplefilter(action='ignore', category=FutureWarning) # 1. Helper Functions ## 1.1 Confusion Matrix Confusion matrices are an important toolkit in every data scientist's box. We create …