CSI, Balanced accuracy, and FMI Analysis.

<div><p>Identifying mitosis is crucial for cancer diagnosis, but accurate detection remains difficult because of class imbalance and complex morphological variations in histopathological images. To overcome this challenge, we propose a Customized Deep Learning (CDL) model, which integrat...

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Bibliographic Details
Main Author:	Afnan M. Alhassan (18349378) (author)
Other Authors:	Nouf I. Altmami (21696156) (author)
Published:	2025
Subjects:	Cell Biology Cancer Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified walrus optimization algorithm trained deep networks jellyfish search optimizer integrates advanced deep include fusion methodologies hereby bring forth greatly empower pathologists excellent f1 score complex morphological variations benchmark dataset displays innovative selection mechanism bolster feature extraction customized deep learning better localize mitosis alleviate class imbalance improved mitotic detection better mitosis detection cdl model comprises feature selection class imbalance mitotic figures mitosis detection transfer learning learning techniques mitosis datasets time efficiency time applications thus proving skip connections robust assessment rigorously evaluated proposed methodology proposed approach outstanding efficacy histopathological images future lines extracted features extending cdl cdl model cancer diagnosis also employed accurate appraisal
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CSI, Balanced accuracy, and FMI Analysis.

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