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- W3035186293 abstract "Object detection is an important and complex task in computer vision because detection requires precise location of objects. This creates two problems: the first is that many locations (“proposals”) must be processed. The second is that the approximate location of the proposals must be specified to obtain a specific location. These articles present an approach to detecting a lung tumor. Our goal is to use the ASM (Active Form Model) and try to correct its defects related to the learning phase. To do this, we proposed the development of automated markers in which we integrate the DNN (deep neural network) learning method and automated storage at the detection stage. Finally, we evaluated the performance of the classifier according to certain criteria such as RMS (mean square error). Experimental results show that the deep neural network has archived the best performances with precision and sensitivity, specificity. These recent developments not only allow a better diagnosis but also offer new hopes of treatment for many diseases (cancer, epilepsy… etc.)." @default.
- W3035186293 created "2020-06-19" @default.
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- W3035186293 date "2019-12-01" @default.
- W3035186293 modified "2023-09-27" @default.
- W3035186293 title "A Novel Approach of Lung Tumor Detection Using Deep Neural Network" @default.
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- W3035186293 doi "https://doi.org/10.1109/iintec48298.2019.9112100" @default.
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