We propose Nazr-CNN 1 , a deep learning pipeline for object detection and fine-grained classification in images acquired from Unmanned Aerial Vehicles (UAVs) for damage assessment and monitoring. Nazr-CNN consists of two components. The function of the first component is to localize objects (e.g. houses or infrastructure) in an image by carrying out a pixel-level classification. In the second component, a hidden layer of a Convolutional Neural Network (CNN) is used to encode Fisher Vectors (FV) of the segments generated from the first component in order to help discriminate between different levels of damage.To showcase our approach we use data from UAVs that were deployed to assess the level of damage in the aftermath of a devastating cyclone that hit the island of Vanuatu in 2015. The collected images were labeled by a crowdsourcing effort and the labeling categories consisted of fine-grained levels of damage to built structures. Since our data set is relatively small, a pretrained network for pixel-level classification and FV encoding was used. Nazr-CNN attains promising results both for object detection and damage assessment suggesting that the integrated pipeline is robust in the face of small data sets and labeling errors by annotators. While the focus of Nazr-CNN is on assessment of UAV images in a post-disaster scenario, our solution is general and can be applied in many diverse settings. We show one such case of transfer learning to assess the level of damage in aerial images collected after a typhoon in Philippines. 1 Nazr means "sight" in Arabic. 2

The enlarged veins in the pampiniform venous plexus, known as varicocele disease, are typically identified using ultrasound scans. The medical diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male patient. The proposed system is designed to determine whether a patient is affected. Varicocele is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other positions. Therefore, the experimental results of this study focused on images taken in the supine position of the left testicles of patients. There are two possible vein structures in each image: a cross-section (circular) and a tube (non-circular) structure. This proposed system identifies dilated (varicocele) veins of these structures in ultrasound images in three stages: preprocessing, processing, and detection and measurement. These three stages are applied in three different color modes: Grayscale, Red-Green-Blue (RGB), and Hue, Saturation, and Value (HSV). In the preprocessing stage, the region of interest enclosing the pampiniform plexus area is extracted using a median filter and threshold segmentation. Then, the processing stage employs different filters to perform image denoising. Finally, edge detection is applied in multiple steps and the detected veins are measured to determine if dilated veins exist. Overall implementation results showed the proposed system is faster and more effective than the previous work.

Image analysis is an important technique that can help specialists localize, detect and segment objects in different types of medical images such as MRI, CTs, and Ultrasounds (US). In this research, we use US images to identify and segment the enlarged veins in the pampiniform venous plexus, which is called varicocele. The proposed method aims to determine whether a potential patient is affected or not. This method was evaluated on 90 US images that were taken to the left testicles of 90 patients using the Supine position. This system analyzes US images in three stages which are; preprocessing, processing, and edge detection. The Region Of Interest (ROI) of the pampiniform plexus area was extracted using Otsu segmentation with different parameters 0.1, 0.2, and 0.17, and different color modes (Grayscale, YCbCr, RGB). In the processing stage, different denoising filters were used. Eventually, in the edge detection stage, four edge detectors were applied which are Canny, Soble, Prewitt, and Roberts. Results showed that the best accuracy in detecting varicocele was 78% when YCbCr color mode yellow (y) channel is used with 0.1 Otsu segmentation and the Canny edge detector. The system also showed a Sensitivity of 91%, the test was able to detect 91% of the people with Varicocele, and a Specificity of 39%.

No abstract

No abstract