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A Novel Deep Learning Algorithm for the Automatic Detection of High-Grade Gliomas on T2-Weighted Magnetic Resonance I mages: A Preliminary Machine Learning Study
No full textAIM: To propose a convolutional neural network (CNN) for the automatic
detection of high-grade gliomas (HGGs) on T2-weighted magnetic resonance
imaging (MRI) scans.
MATERIAL and METHODS: A total of 3580 images obtained from 179
individuals were used for training and validation. After random rotation
and vertical flip, training data was augmented by factor of 10 in each
iteration. In order to increase data processing time, every single image
converted into a Jpeg image which has a resolution of 320x320. Accuracy,
precision and recall rates were calculated after training of the
algorithm.
RESULTS: Following training, CNN achieved acceptable performance ratios
of 0.854 to 0.944 for accuracy, 0.812 to 0.980 for precision and 0.738
to 0.907 for recall. Also, CNN was able to detect HGG cases even though
there is no apparent mass lesion in the given image.
CONCLUSION: Our preliminary findings demonstrate; currently proposed CNN
model achieves acceptable performance results for the automatic
detection of HGGs on T2-weighted images
Erratum: Ancient pigs reveal a near-complete genomic turnover following their introduction to Europe (Proceedings of the National Academy of Sciences of the United States of America (2019) 116 (17231-17238) DOI: 10.1073/pnas.1901169116)
No full textIs gluteus medius injured in patients treated with a trochanter tip entry intramedullary nail? Clinical, electrophysiological and functional outcomes
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Posterior fossa horns; a new calvarial finding of mucopolysaccharidoses with well-known cranial MRI features
No full textValidity and Reliability of the Turkish Version of the London Chest Activity of Daily Living Scale in Obstructive Lung Diseases
No full textLessons learned so far from the pandemic: A review on pregnants and neonates with COVID-19
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