Microcirculation in a subject can be examined and pathological changes can be assessed by utilizing capillaroscopy, which is a very safe, convenient and non-invasive approach. Using a microscope, doctors view the capillaries by looking through nailfold epidermis. Nailfold anatomy is ideal to evaluate the microcirculation and detect various diseases caused by vascular damages. Rheumatologists evaluate systemic diseases which involve damage in vasculature, by analyzing the red blood cells within the capillaries. Sometimes, capillary morphology may be useful as an early indicator while, severity of damage in capillary architecture may indicate internal organ involvement. Thus, in a capillaroscopic assessment, the doctor examines modifications in morphological and functional aspects of capillaries. These comprise of capillary diameter, visibility, distribution, length, microhemorrhages, blood flow and density. In this paper, a novel object detection algorithm is proposed based on deep learning architectures for detecting and locating various capillary loops in the nailfold region. Various characteristic features are extracted from the capillaries through image processing algorithms and in turn an attempt is made to differentiate between images of diseased subjects and healthy controls.

<span lang="EN-US">Non-destructive evaluation (NDE) is very essential in measuring the properties of materials and in turn detect flaws and irregularities. Pulsed thermography (PT) is one of the advanced NDE technique which is used for detecting and characterizing subsurface defects. Recently many methods have been reported to enhance the signal and defect visibility in PT. In this paper, a novel unsupervised deep learning-based auto-encoder (AE) approach is proposed for enhancing the signal-to-noise ratio (SNR) and visualize the defects clearly. A detailed theoretical background of AE and its application to PT is discussed. The SNR and defect detectability results are compared with the existing approaches namely, higher order statistics (HOS), principal component thermography (PCT) and partial least square regression (PLSR) thermography. Experimental results show that AE approach provides better SNR at the cost of defect detectability.</span><br /><div id="ext-mouse-move" style="display: none;"> </div><div id="ext-mouse-down" style="display: none;"> </div><div id="ext-mouse-up" style="display: none;"> </div>