Disease Classification of Liver and Lungs Using Deep learning

Lung disease is among the leading diseases that cause mortality worldwide. Most cases of lung diseases are detected when the disease is in the advanced stages. Therefore the development of systems and methods that enable faster and early diagnosis will play a vital role in the world today. Computer Aided Diagnosis (CADx) systems play such a role and are currently being expanded. This study explores the potential of using deep learning features from pre-trained deep learning architectures to provide rich and robust features. These features were compared to the conventionally used Gray-level Co-occurrence Matrix (GLCM). Deep features produced the highest accuracy of 100% as compared to 93.52% produced by using GLCM features. This study also compared the classification of deep features with five different classifiers and Support Vector Machine (SVM) showed the highest result. This high accuracy was also reproduced with Linear Discriminant Analysis (LDA) and Regression classifiers. Principal Component Analysis (PCA) was also done to evaluate the usage of reduced number of features and its effect on the classification performance.

Keyword: Medicinal, deep learning, Mobile net model, resnet, minutiae