Deep-Lung Diagnosis – Deep Learning-Based Lung Disease Classification and Detection Using CNN and ResNet Models

Deep-Lung Diagnosis – Deep Learning-Based Lung Disease Classification and Detection Using CNN and ResNet Models

Shreyas B K1, Dr. H K Madhu2

1 Student, Department of MCA, Bangalore Institute of Technology, Karnataka, India

2Associate Professor, Department of MCA, Bangalore Institute of Technology, Karnataka, India

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Abstract - The classification, detection and certainty measurement of lung x-ray image anomalies using advanced sophisticated deep learning techniques such as CNN have witnessed a growth betterment especially to reach a working solution that focus on chest illnesses e.g. asthma, pneumonia, TB,COVID-19, lung cancer. Numerous datasets have been used to test the model to the maximum extend its capability and its accuracy message seems well received. Additionally, a user-friendly graphical interface allows medical practitioners to quickly assess and interpret x-ray images with precise outcomes based on CNN. The aim is to integrate powerful algorithms of deep learning into medical practice so as to establish faster and more reliable diagnostic tools for chest diseases that will enable early intervention, personalized treatment options as well as improved patient outcomes.

Key Words:  Deep-Lung Diagnosis, Convolutional neural network, Mask CNN, Lung Disease classification, X-Ray Images.