CeliacNet: Celiac Disease Severity Diagnosis on Duodenal Histopathological Images Using Deep Residual Networks Celiac Disease (CD) is a chronic autoimmune disease that affects the small intestine in genetically predisposed children and adults. Gluten exposure triggers an inflammatory cascade which leads to compromised intestinal barrier function. If this enteropathy is unrecognized, this can lead to anemia, decreased bone density, and, in longstanding cases, intestinal cancer. The prevalence of the disorder is 1% in the United States. An intestinal (duodenal) biopsy is considered the xe2x80x9cgold standardxe2x80x9d for diagnosis. The mild CD might go unnoticed due to nonspecific clinical symptoms or mild histologic features. In our current work, we trained a model based on deep residual networks to diagnose CD severity using a histological scoring system called the modified Marsh score. The proposed model was evaluated using an independent set of 120 whole slide images from 15 CD patients and achieved an AUC greater than 0.96 in all classes. These results demonstrate the capability of the proposed model for CD severity classification using histopathological images.

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.