In our final project at T5 bootcamp we will build models that can detect the presence of pneumonia with chest X-rays using Deep Convololutional Neural Network. Chest X-rays are currently the best available method for diagnosing pneumonia, playing a crucial role in clinical care. Over 150 million people get infected with pneumonia on an annual basis especially children under 5 years old.

Providing a clinical aid for higher accuracy image reads of diseases that are hard to diagnose. Clinical diagnoses from chest X-rays can be challenging, however, even by skilled radiologists. The diagnosis of pneumonia from chest X-rays is difficult for several reasons:

1. The appearance of pneumonia in a chest X-ray can be very vague depending on the stage of the infection
2. Pneumonia often overlaps with other diagnoses

The dataset is organized into 3 folders (train, test, validation). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal). Chest X-ray images (anterior-posterior) were selected of pediatric patients of one to five years old.

• High accuracy
• Less medical test
• Low cost
• Reduce time
• Reduce death rate
• Getting knowledge about machine learning techniques
• Determining binary classification of lung diseases

• Sequential
• Vgg16
• Vgg19
• MobileNetV2
• InceptionV3

Languages : Python Tools/IDE : Google colab Libraries : numpy, pandas, matplotlib, Keras, TensorFlow

• @Abdullatif Alsabr
• @Yazeed Alharthi
• @Aljoharah Alaqeel
• @Eman Albarakah
• @Rahaf Alturbaq