White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning.

White blood cells (WBCs) are an indispensable constituent of the immune system. Efficient and accurate categorization of WBC is a critical task for disease diagnosis by medical experts. This categorization helps in the correct identification of medical problems. In this research work, WBC classes are categorized with the help of a transform learning model in combination with our proposed virtual hexagonal trellis (VHT) structure feature extraction method. The VHT feature extractor is a kernel-based filter model designed over a square lattice. In the first step, Graft Net CNN model is used to extract features of augmented data set images. Later, the VHT base feature extractor extracts useful features. The CNN-extracted features are passed to ant colony optimization (ACO) module for optimal features acquisition. Extracted features from the VHT base filter and ACO are serially merged to create a single feature vector. The merged features are passed to the support vector machine (SVM) variants for optimal classification. Our strategy yields 99.9% accuracy, which outperforms other existing methods.

Coronary Artery Ectasia: Clinical and Angiographic Features.

OBJECTIVE: To investigate the clinical and angiographic characteristics of coronary artery ectasia (CAE) and its relation with the inflammatory marker, HsCRP. STUDY DESIGN: An observational study. PLACE AND DURATION OF STUDY: Rawalpindi Institute of Cardiology, Rawalpindi, from April 2015 till November 2016. METHODOLOGY: Eighty-one patients with CAE and 57 age matched patients with stenotic coronary artery disease (CAD), but without CAE, were included in the study. Clinical, angiographic, and laboratory data were documented. Chi-square test was used to compare coronary risk factors between two groups. T test was used to compare means between the groups. Analysis of variance was used to analyse HsCRP levels among various types of ectasia. Correlation analysis was used to study association of ectasia with different risk factors. RESULTS: Males were predominant in both with & without CAE. Hypertension, smoking and obesity were significantly more common among CAE patients than those without (60.5% vs. 52.6%, 56.8% vs. 43.9% and 80.2% vs. 14%, respectively). Diabetes was much less in CAE group (32.1% vs. 42.1%). HsCRP was higher in patients with CAE than those without and was significantly higher in patients with more extensive ectasia. Majority (65.4%) of CAE patients had significant CAD; whereas, only 7.4% had isolated CAE. Most common artery involved was RCA (70.4% of total) and most common pattern was single ectatic vessel. CONCLUSION: Obesity and smoking predispose to CAE, along with male sex and hypertension. While diabetes is negatively associated with CAE. HsCRP levels tend to be higher in ectasia patients, especially those with severe forms. Finally, CAE has a predilection for RCA.