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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Liver cyst wall - high mag.jpg|220px]]</div>
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'''[[Histopathology]]''' is a branch of histology and pathology that studies and diagnoses diseases on the tissue and cellular level. While histopathology is closely related to [[cytopathology]], the main difference is diagnostic information gained from histopathology is acquired from solid tissue samples, whereas specific disaggregated cell preparations are used in cytopathology. Typically a biopsy or surgical specimen is examined by a pathologist after the specimen has been processed and histological sections have been placed onto glass slides. Testing typically incorporates several stages, from collection and preparation using numerous methods (depending on the sample and test type) down to examination.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Rudolf Ludwig Karl Virchow is considered by many to be one of the fathers of cellular pathology, remembered most for his collection of lectures on the topic, published as ''Cellular Pathology'' in 1858. However, his assistant, David Paul von Hansemann also played an important role in the progress of histopathology during the 1890s, producing his book ''The Microscopic Diagnosis of Malignant Tumours'' and other important research. ('''[[Histopathology|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
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Latest revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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