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October 18, 2025Journal of Innovative Image Processing

Deep Learning Model with Enhanced Segmentation and Combined Feature Activation for Mitosis Classification

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Authors

JLJithy LijoJSJ. S. SaleemaTBTina Babu

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Overview

The automated detection method improves mitosis classification accuracy, suggesting a reliable tool for cancer diagnosis.

Key Points

  • The proposed ATSO-Deep CNN model achieved a remarkable accuracy of 96.31% in mitosis classification.
  • Classifications also showed an F1-score of 96.3%, precision of 96.84%, and recall of 95.78%, enhancing diagnostic confidence.
  • This framework optimally trains a Deep CNN, improving convergence efficiency while minimizing the false rate.
  • Using the BreCaHAD dataset, this model addresses the critical challenges of detecting mitotic figures in cancer prognosis.

Cite This Study

Lijo et al. (2025) studied this question.

synapsesocial.com/papers/68f35bfc73f0a7d050f47f2fhttps://doi.org/10.36548/jiip.2025.4.006
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Also Consider

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  1. 1Efficient mitosis detection: leveraging pre-trained faster R-CNN and cell-level classification2024 · 6 citations
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  5. 5Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images2024 · 1 citations