Floods are one of the most devastating and frequently occurring natural disasters throughout the world. Floods can cause blockage of roads and hence create trouble for civilians and authorities to navigate in the flooded area. This paper proposes an automated system that uses a road extraction algorithm to extract roads from satellite images to create a highlighted map of all the available roads during floods. The road extraction algorithm the authors developed uses U-net model architecture, a fully convolutional neural network, to extract roads from aerial images (satellite images and drone images). Convolutional Neural Network is robust to shadows and water streams, able to obtain the characteristics of roads adequately and most importantly, able to produce output quickly, which is necessary for flood evacuations and relief. The developed system can be deployed as an Application Programming Interface or stand-alone system, loaded on drones, which will provide the users with a map highlighting safe paths to traverse the flooded areas.
Filariasis is a major public health problem in the tropics. The involvement of breast, more so in men, is a rare event even in the endemic areas. In this case report, we present an elderly man with a painless breast lump with microfilarial involvement. A triple evaluation was done initially to rule out malignancy. Examination revealed a discrete, firm, retroareolar lump. Ultrasonography showed soft tissue thickening and edema in subcutaneous plane suggestive of inflammatory pathology. FNAC revealed microfilariae in a background of leucocytes. Diagnosis of Bancroftian filariasis was made and patient was started on oral diethylcarbamazine. After 3 weeks of treatment the swelling resolved completely and the patient is asymptomatic for more than one year.
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