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<p>Breast cancer is a significant global health issue, demanding early identification to provide appropriate therapy and satisfactory survival results. This work used two independent imbalanced datasets (EIS-BT and WBCD). CNN1D with Long Short-Term Memory (LSTM) was integrated to acquire features from these datasets to identify breast cancer. Three scenarios for breast cancer detection were investigated based on CNN1D-LSTM derived characteristics from Dataset-1, Dataset-2, and their combination. The Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the collected features in all three scenarios. The suggested CNN1D-LSTM-SMOTE approach, in conjunction with Support Vector Classification (SVC), yields impressive results with a Matthews Correlation Coefficient (MCC) of 97.2 % on Dataset-1 and 100.0% on Dataset-2. Random Forest Classifiers (RFC) perform better, achieving an MCC of 98.4% on the combined features. The K-fold approach was used, yielding average MCCs of 91.7%, 74.1%, and 96.9% on Dataset-1, Dataset-2, and the combined features, respectively. Statistical analysis revealed a p-value of 0.01, signifying the significance of the findings, and a standard error of 0.006 for the combined features. Bootstrapping was employed to calculate confidence intervals, resulting in Lower Confidence Intervals (LCI) of 95.6% and Higher Confidence Intervals (HCI) of 97.9% for the combined features. These findings highlight the model’s potential clinical application in supporting oncologists with early, real-time, reliable, and automated breast cancer diagnosis, leading to improved diagnostic procedures and patient outcomes.</p>

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CNN1D-LSTM with SMOTE for breast cancer classification: performance and statistical insights

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Kamini G. Panchbhai,
  • Lalchand B. Patle,
  • Madhusudan G. Lanjewar

摘要

Breast cancer is a significant global health issue, demanding early identification to provide appropriate therapy and satisfactory survival results. This work used two independent imbalanced datasets (EIS-BT and WBCD). CNN1D with Long Short-Term Memory (LSTM) was integrated to acquire features from these datasets to identify breast cancer. Three scenarios for breast cancer detection were investigated based on CNN1D-LSTM derived characteristics from Dataset-1, Dataset-2, and their combination. The Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the collected features in all three scenarios. The suggested CNN1D-LSTM-SMOTE approach, in conjunction with Support Vector Classification (SVC), yields impressive results with a Matthews Correlation Coefficient (MCC) of 97.2 % on Dataset-1 and 100.0% on Dataset-2. Random Forest Classifiers (RFC) perform better, achieving an MCC of 98.4% on the combined features. The K-fold approach was used, yielding average MCCs of 91.7%, 74.1%, and 96.9% on Dataset-1, Dataset-2, and the combined features, respectively. Statistical analysis revealed a p-value of 0.01, signifying the significance of the findings, and a standard error of 0.006 for the combined features. Bootstrapping was employed to calculate confidence intervals, resulting in Lower Confidence Intervals (LCI) of 95.6% and Higher Confidence Intervals (HCI) of 97.9% for the combined features. These findings highlight the model’s potential clinical application in supporting oncologists with early, real-time, reliable, and automated breast cancer diagnosis, leading to improved diagnostic procedures and patient outcomes.