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<p>Breast cancer, which is one of the most lethal forms of cancer in women, continues to pose a significant threat to public health. The World Health Organization (WHO) reports that recent data highlight the significance of this issue, revealing a mortality rate of over 6% among breast cancer patients and a mere 8% early detection rate. This paper introduces the Extraterrestrial Spacecraft Mineral Extraction Optimization Algorithm (ESMXO) and the Deep Residual Attention and Multivariate Sequencing Model for breast cancer lesion research. ESMXO is a distinctive optimization algorithm that draws inspiration from satellite navigation and mineral extraction operations in order to enhance the accuracy of bioinformatics forecasts. This innovative approach integrates convolutional neural networks (CNNs) such as Xception and NASNetMobile to analyze intricate patterns in cancer sequences and identify subtle genetic variations associated with certain phenotypic outcomes. The results of this approach are highly encouraging, demonstrating consistent effectiveness at various levels of magnification. Under a magnification of 100×, the algorithm achieved perfect scores in all metrics, consistently falling within the range of 1.0000 ± 0.005 to 0.008. When magnified at 200×, the accuracy and recall were found to be 0.9912 ± 0.005 to 0.008, indicating a high level of consistency. Even when magnified at 400×, the model demonstrates a high level of accuracy within the range of 0.9470 ± 0.005 to 0.008. At a magnification of 40×, the model consistently achieved an impressive accuracy of 0.9923 ± 0.005 to 0.008. The dataset's magnification greatly improves machine accuracy for the lab technician, enabling more effective therapy in various stages and aspects of breast cancer. The models demonstrate exceptional accuracy throughout the dataset, particularly with a broad range from 40× to 400×. The Deep Residual Attention Network with Xception and NASNet (DRAN-XN) outperforms current methods in accuracy, sensitivity, and specificity for breast cancer.</p>

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A Comprehensive Study of Enhanced Computational Approaches for Breast Cancer Classification: Comparative Analysis with Existing State of the Art Methods

  • 期刊论文

  • 发表日期：

    2025年10月8日

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  • Davinder Paul Singh,
  • Tathagat Banerjee,
  • Pawandeep Kour,
  • Rahul Malik,
  • Gangu Rama Naidu,
  • Anand Deva Durai C,
  • Raju Kumar,
  • Rajanish Kumar Kaushal,
  • Ram Murat Singh,
  • Yogendra Narayan

摘要

Breast cancer, which is one of the most lethal forms of cancer in women, continues to pose a significant threat to public health. The World Health Organization (WHO) reports that recent data highlight the significance of this issue, revealing a mortality rate of over 6% among breast cancer patients and a mere 8% early detection rate. This paper introduces the Extraterrestrial Spacecraft Mineral Extraction Optimization Algorithm (ESMXO) and the Deep Residual Attention and Multivariate Sequencing Model for breast cancer lesion research. ESMXO is a distinctive optimization algorithm that draws inspiration from satellite navigation and mineral extraction operations in order to enhance the accuracy of bioinformatics forecasts. This innovative approach integrates convolutional neural networks (CNNs) such as Xception and NASNetMobile to analyze intricate patterns in cancer sequences and identify subtle genetic variations associated with certain phenotypic outcomes. The results of this approach are highly encouraging, demonstrating consistent effectiveness at various levels of magnification. Under a magnification of 100×, the algorithm achieved perfect scores in all metrics, consistently falling within the range of 1.0000 ± 0.005 to 0.008. When magnified at 200×, the accuracy and recall were found to be 0.9912 ± 0.005 to 0.008, indicating a high level of consistency. Even when magnified at 400×, the model demonstrates a high level of accuracy within the range of 0.9470 ± 0.005 to 0.008. At a magnification of 40×, the model consistently achieved an impressive accuracy of 0.9923 ± 0.005 to 0.008. The dataset's magnification greatly improves machine accuracy for the lab technician, enabling more effective therapy in various stages and aspects of breast cancer. The models demonstrate exceptional accuracy throughout the dataset, particularly with a broad range from 40× to 400×. The Deep Residual Attention Network with Xception and NASNet (DRAN-XN) outperforms current methods in accuracy, sensitivity, and specificity for breast cancer.