2024
DOI: 10.1007/s11042-024-18505-4
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Rapid tri-net: breast cancer classification from histology images using rapid tri-attention network

Pallavi Bhanudas Salunkhe,
Pravin Sahebrao Patil
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Cited by 1 publication
(2 citation statements)
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“…For 40X magnified images, the A stable training and testing accuracy was achieved after 12 epochs, and more than 90% accuracy was achieved after approximately 12 epochs. For 40X magnified images, the 1 2 3 4 5 6 7 8 9 10 11 12 13 A stable training and testing accuracy was achieved after 12 epochs, and more than 90% accuracy was achieved after approximately 12 epochs. For 40X magnified images, the model's accuracy is maximized, i.e., 99%, and for the remaining magnification factor, it achieved 96% accuracy.…”
Section: Resultsmentioning
confidence: 93%
See 1 more Smart Citation
“…For 40X magnified images, the A stable training and testing accuracy was achieved after 12 epochs, and more than 90% accuracy was achieved after approximately 12 epochs. For 40X magnified images, the 1 2 3 4 5 6 7 8 9 10 11 12 13 A stable training and testing accuracy was achieved after 12 epochs, and more than 90% accuracy was achieved after approximately 12 epochs. For 40X magnified images, the model's accuracy is maximized, i.e., 99%, and for the remaining magnification factor, it achieved 96% accuracy.…”
Section: Resultsmentioning
confidence: 93%
“…Their model received 94.37% binary classification on the BreakHis dataset [12]. In [13], authors preprocessed images for noise removal and enhancement using weighted guided filtering and Gamma correction. Later, a hierarchical approach was presented to extract optimal structural features using superpixel mixed clustering, GLCM, and optimization algorithms.…”
Section: Related Workmentioning
confidence: 99%