Automatic identification of wildfires is an area attracting great interest in the past decade. Early detection of fire can help in minimizing disasters and assist decision makers to plan mitigation methods. In this paper, we annotate and utilize a drone imagery dataset with each of its pixels marked as: (a) Burning, (b) Burned, and (c) Unburnt. The dataset is comprised of 22 videos (138,390 frames) among which only a subset of 481 frames (~20 frames from each video) are marked for segmentation. In addition, the entire suite of frames is categorized as either “Smoke” or “No-Smoke”. We implement DeepLab-v3+ architecture to accurately segment affected regions as “Burned”, “Burning”, and “Unburnt”. We adopt a transfer learning-based architecture using an established Xception network to detect smoke within each frame to identify regions that can affect the performance of the proposed segmentation approach. Our segmentation algorithm achieves a mean accuracy of 97% and mean Jaccard Index of 0.93 on three test videos comprising 24,666 frames across all categories. Our classification algorithm achieves 92% for identifying smoke in each of those test frames.
Approximately two million pediatric deaths occur every year due to Pneumonia. Detection and diagnosis of Pneumonia plays an important role in reducing these deaths. Chest radiography is one of the most commonly used modalities to detect pneumonia. In this paper, we propose a novel two-stage deep learning architecture to detect pneumonia and classify its type in chest radiographs. This architecture contains one network to classify images as either normal or pneumonic, and another deep learning network to classify the type as either bacterial or viral. In this paper, we study and compare the performance of various stage one networks such as AlexNet, ResNet, VGG16 and Inception-v3 for detection of pneumonia. For these networks, we employ transfer learning to exploit the wealth of information available from prior training. For the second stage, we find that transfer learning with these same networks tends to overfit the data. For this reason we propose a simpler CNN architecture for classification of pneumonic chest radiographs and show that it overcomes the overfitting problem. We further enhance the performance of our system in a novel way by incorporating lung segmentation using a U-Net architecture. We make use of a publicly available dataset comprising 5856 images (1583 – Normal, 4273 – Pneumonic). Among the pneumonia patients, 2780 patients are identified as bacteria type and the rest belongs to virus category. We test our proposed algorithm(s) on a set of 624 images and we achieve an area under the receiver operating characteristic curve of 0.996 for pneumonia detection. We also achieve an accuracy of 97.8% for classification of pneumonic chest radiographs thereby setting a new benchmark for both detection and diagnosis. We believe the proposed two-stage classification of chest radiographs for pneumonia detection and its diagnosis would enhance the workflow of radiologists.
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