Application of CNN Model on Medical Images (Chest X-Rays)

Application of CNN Model on Medical Images (Chest X-Rays)

Vrishabh Bansal1, Mehul Lahot2, Kunal3
Department of Information Technology, Maharaja Agrasen Institute of Technology affiliated to Guru Gobind Singh Indraprastha University, Rohini, Delhi

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Abstract - Chest diseases encompass a range of conditions affecting the lungs, including infections caused by bacteria, viruses, or other microorganisms. These diseases can vary in severity, posing a potential threat to vulnerable individuals such as young children, older adults, and those with weakened immune systems. Detecting chest diseases involves employing diverse methods, such as physical examination, chest x-rays, blood tests, and sputum culture. During a physical examination, healthcare professionals utilize a stethoscope to listen for abnormal breathing sounds or crackling noises in the chest. They also assess factors like fever and respiratory rate. Chest x-rays provide a visual representation of the lungs, enabling the identification of areas displaying inflammation or fluid accumulation. Sputum culture involves collecting a sample of mucus expelled from the lungs through coughing and testing it for the presence of microorganisms.

Key Words: Deep Learning, CNN, Chest X-Ray Images.