Advanced and Minor Lung Disease Severity Classification Using Deep Features A key component of Computer Aided Diagnosis (CADx) systems is classification. This stage involves feature extraction. Deep features have shown to be an emerging area of research in various fields including medical imaging. Most recent deep works in the lung area focused using Convolution Neural Networks (CNN). However, these works have drawbacks such as overclassifying and are not reflective of the real world. Therefore this studyu0027s aim is to develop a lung disease severity classification (LDSC) system. Information on lung disease severity can be beneficial to clinicians to plan treatments. This study used high resolution computed tomography (HRCT) scans from Hospital Kuala Lumpur where there were 81 diseased (Interstitial Lung Disease) and 15 normal patients. Deep features were obtained using a pretrained network (Alexnet). This studied used four classifiers Support Vector Machine (SVM), Naive Bayes, Random Forest and KNN. A high accuracy of 100% was achieved using SVM.

Lung Disease Classification using Deep Convolutional Neural Network The advanced technologies are essential to achieving the improvement of medicine. More specifically, an extensive investigation in a partnership among researchers, health care providers, and patients is integral to bringing precise and customized treatment strategies in taking care of various diseases. This paper aims to assess the degree of accuracy acceptable in the medical field by utilizing deep learning to publicly available data. First, we extracted spectrogram features and labels of the annotated lung sound samples and used them as an input to our 2D Convolutional Neural Network (CNN) model. Secondly, we normalized the lung sounds to remove the peak values and noise from them. For deep learning classification, publicly available data was not sufficient to conduct the learning process. Finally, we have created a deep learning model called Lung Disease Classification (LDC), combined with advanced data normalization and data augmentation techniques, for highperformance classification in lung disease diagnosis. The final accuracy obtained after the normalization and augmentation was approximately 97%. The proposed model paves the way for adequate assessment of the degree of accuracy acceptable in the medical field and guarantees better performance than other previously reported approaches.