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Gastric Cancer Detection by Two-step Learning in Near-Infrared Hyperspectral Imaging
Abstract: Gastric cancer is one of the most serious cancers that affects and kills many people around the world every year. Early treatment of gastric cancer dramatically improves the survival rate. Endoscopy has become an important tool for early detection. Since invasive gastric cancer or the edge of the invasive gastric cancer is difficult to find by using conventional visible-light endoscopy, near-infrared imaging, which is bringing great progress to the medical field, is focused on in recent years. In order to apply near-infrared hyperspectral imaging (NIR-HSI) in real-time, wavelength feature extraction is important because a large amount of data needs to be analyzed. The purpose of this study is to detect gastric cancer using NIR-HSI and to select a suitable wavelength for the target in the near-infrared region (1000–1600 nm). NIR-HSI was used to take data from six specimens of gastric cancer and each pixel was labeled as normal or tumor on the hyperspectral image based on the histopathological diagnosis. 4 wavelengths were extracted from 95 wavelengths using the least absolute shrinkage and selection operator method. Supervised learning was performed using a support vector machine for both cases using all 95 wavelengths and the case using 4 selected wavelengths. In both cases, the approximate location of the tumor could be identified, indicating that an appropriate wavelength could be selected. We were also able to improve the detection accuracy by creating new supervised data and adding another learner. The detection accuracy was 93.3% for accuracy, 69.8% for sensitivity, and 96.7% for specificity. These results show that gastric cancer can be detected even at four wavelengths. By applying the results of this study to the endoscope system, the possibility of constructing a NIR endoscope system for gastric cancer was suggested.
Keywords: Cancer detection; Gastric cancer; Near-infrared hyperspectral imaging; The least absolute shrinkage and selection operator; Support vector machine
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Non-Predefined Life Signs Detection for Disaster Survivors Rescue
Abstract: No one can tell or predict when and where a natural disaster such as an earthquake or tornado will occur and the damages they may cause. Or an overflow of a large amount of water beyond its normal limit (water flood) shallowing city as we used to watch on news TV after the passage of a strong typhoon causing heavy rain. These natural disasters occur every day and anywhere around the globe are not new. And we cannot not prevent them from occurring in spite of the best technology we have in our possession now. But saving lives after their occurring is still possible and the best technology for this is the combination of AUV and the image processing. Image processing is one of the best ever invented technology by human since the course on technology development between scientists for sustainable development of our society. In order word “image processing is the technology that meets the needs of the 21st society we live in without compromising the ability of future generation to meet what they need to make the use of this technology efficiency. This paper proposes non-predefined life signs detection for disaster survivors rescue when a disaster occur and especially during a floodwater. In this research we use the matrix-based pairs of opposing pixels positioned directly around the observed point that belongs to the edge of the life signs target. At first one-dimension matrix for bitmap memorization values of the RGB components of pixel is used. Next these values of the RGB components of pixel color are copied from bitmap to matrix. The number of bytes in a row is rounded up to the nearest number divisible by four. As a result, the method clearly detects all life signs edge made by human using any type of item around them with 95%.
Keywords: Life signs; Image processing; Disaster; Edge detection; Rescue
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Seashore Debris Detection Model with KaKaXi Camera Custom Dataset Using Instance Segmentation
Abstract: Marine debris is impacting coastal landscapes majorly by affecting biodiversity, impairing recreational uses, causing losses to fishing industries, maritime industries, etc. Motivated by the need for automatic and cost-effective approaches for debris monitoring and removal, we employed computer vision technique together with deep learning-based model to identify and classify marine debris on several beach locations. This paper provides a comparative analysis of state-of-the-art deep learning architectures and proposed architecture which is used as feature extractor for debris image classification.
The model is being proposed to detect seven categories of marine debris using a custom debris dataset, with the help of instance segmentation and a shape matching network, which can then be cleaned timely and efficiently. The manually constructed dataset for this system is created by annotating fixed KaKaXi camera images using CVAT with seven types of labels. A pre-trained HOG shape feature extractor is being used on LIBSVM along with template matching to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the recorded live debris data. The proposed network resulted in the improvement of misclassification of debris masks for objects with different illuminations, shape, occlusion and viewpoints.
Keywords: debris; fixed camera images; computer vision; instance segmentation; deep learning; template matching; Histogram of Gradients (HOG)
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JICE Vol 2 Issue 4
Open AccessPublished 10th December 2016, ISSN 2432-5465 , Total Pages 8
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JICE Vol 2 Issue 3
Open AccessPublished 22nd April 2016, ISSN 2186-9162, Total Pages 4
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JICE Vol 2 Issue 2
Open AccessPublished 25th February 2016, ISSN 2186-9154, Total Pages 59
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JICE Vol 1 Issue 1
Open AccessPublished 25th December 2015, ISSN 2186-9162, Total Pages 65