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Madani, Maryam, Shabani, Hasti. (1405). Advancing Breast Cancer Diagnosis with Deep Layer-wise Feature Integration from Histopathology Images. سامانه مدیریت نشریات علمی, 16(5), 407-424. doi: 10.31661/jbpe.v0i0.2508-1970
Maryam Madani; Hasti Shabani. "Advancing Breast Cancer Diagnosis with Deep Layer-wise Feature Integration from Histopathology Images". سامانه مدیریت نشریات علمی, 16, 5, 1405, 407-424. doi: 10.31661/jbpe.v0i0.2508-1970
Madani, Maryam, Shabani, Hasti. (1405). 'Advancing Breast Cancer Diagnosis with Deep Layer-wise Feature Integration from Histopathology Images', سامانه مدیریت نشریات علمی, 16(5), pp. 407-424. doi: 10.31661/jbpe.v0i0.2508-1970
Madani, Maryam, Shabani, Hasti. Advancing Breast Cancer Diagnosis with Deep Layer-wise Feature Integration from Histopathology Images. سامانه مدیریت نشریات علمی, 1405; 16(5): 407-424. doi: 10.31661/jbpe.v0i0.2508-1970

Advancing Breast Cancer Diagnosis with Deep Layer-wise Feature Integration from Histopathology Images

Journal of Biomedical Physics and Engineering
دوره 16، شماره 5 - شماره پیاپی 1، دی 2026، صفحه 407-424    اصل مقاله (4.45 M)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2508-1970
نویسندگان
Maryam Madani؛ Hasti Shabani*
Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
چکیده
Background: Breast Cancer (BC) remains a leading cause of cancer-related deaths among women. Diagnosis from histopathology images can be inconsistent in pathologist assessments, highlighting the need for reliable computer-aided diagnostic systems. Existing approaches overlook multi-layer features or employ extraction methods that lack generalizability.
Objective: This study proposes a layer-wise feature integration framework to enhance BC histopathology classification.
Material and Methods: In this analytical and computational study, using the BreakHis and ICIAR/BACH datasets, features are extracted from intermediate and high-level layers of pre-trained Convolutional Neural Networks (CNNs). Mutual Information (MI) between each layer’s features and true labels is computed to identify the most informative layers, and those with the highest MI are selected. To address overfitting, Principal Component Analysis (PCA) with optimal Cumulative Explained Variance (CEV) is applied. The transformed features are then concatenated and classified using a Support Vector Machine (SVM).
Results: The method is evaluated using VGG19, DenseNet, and ConvNextTiny across both datasets and demonstrates significant improvements in image-wise and patient-wise classification. ConvNextTiny, enhanced with Synthetic Minority Oversampling Technique (SMOTE) for class-imbalance mitigation, achieves the highest accuracy of 99.14% at 40× in BreakHis dataset. 
Conclusion: Although the achieved accuracies of 97.32% and 99.14% (without and with imbalance handling, respectively), are slightly below the best-reported results, the method offers notable advantages in simplicity and computational efficiency. It provides a lightweight alternative to ensemble-based or fine-tuned deep models. Results highlight the critical role of intermediate layers in capturing discriminative features and challenge reliance on final-layer outputs for histopathology image classification.

تازه های تحقیق

Hasti Shabani (Google Scholar)

کلیدواژه‌ها
Breast Cancer؛ Deep Learning؛ Effective Layers؛ Histopathology Images؛ Convolutional Neural Networks؛ Diagnosis
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