this thesis aims to introduce the world of deep learning and its different applications, mainly focused on its use in the medical field. in the first chapter it starts by introducing deep learning as a concept and discusses its history and the key advancements in this field through history. it also discusses what is the main inspiration of deep learning , it being the human brain functionality, and compares the artificial neural networks or biological neural networks. then proceeds to discuss the different applications and concerns of this technology. in the second chapter, i introduced the medical field and why it needs automation and how deep learning plays a big role in this task citing down certain tasks that can be done through deep learning , how concerns in this field are even higher and more sensetive because human lives rely on it. in the third chapter i introduced my personal work where i applied deep learning to a dataset of x-ray for lung diseases , approaching the prediction problem from two points of view : categorical classifcation and multi-label classification. in this chapter i detailed the architectures of my different base models , highlighted the modifications made and their results as well as which model is the best for this type of data.