Breast Cancer Classification in Ultrasound Imaging Using Cost-Sensitive Learning and K-Means SMOTE on the Imbalanced BUSI Dataset with Deep Feature Extraction

Nedim Muzoglu

Makale detayları

Breast Cancer Classification in Ultrasound Imaging Using Cost-Sensitive Learning and K-Means SMOTE on the Imbalanced BUSI Dataset with Deep Feature Extraction

Yazarlar:

Nedim Muzoglu

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Life and Complex Adaptive Systems

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 2, pp.755-776

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351273

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 Breast cancer is one of the five most common types of cancer that occurs when breast tissue turns into a tumor and mainly affects women. Early diagnosis of the disease is crucial for the patients lifespan. However, misclassification of malignancy may result in treatment delays and initiate an irreversible process for the patient. This study proposes an approach for classifying ultrasound breast images into malignant, benign, and healthy categories, with a particular emphasis on minimizing false-negative outcomes. The BUSI dataset, characterized by imbalanced class distributions, was used for the breast cancer detection. The dataset was augmented to enhance feature representations using contrast-limited adaptive histogram equalization CLAHE to address the class imbalance issue, creating the BUSICL dataset. Features extracted from both datasets with the VGG16 and ResNet50 models were then classified using a support vector machine SVM. Following the results analysis, the SVM algorithms cost matrix values were adjusted according to the inverse proportions of class distributions applying a cost-sensitive approach. In addition, the robustness of the proposed methodology is compared with the K-Means SMOTE algorithm. The proposed method achieved an overall accuracy of 99.36, surpassing the performance of previous comprehensive classification studies using the BUSI dataset.

 Breast cancer is one of the five most common types of cancer that occurs when breast tissue turns into a tumor and mainly affects women. Early diagnosis of the disease is crucial for the patients lifespan. However, misclassification of malignancy may result in treatment delays and initiate an irreversible process for the patient. This study proposes an approach for classifying ultrasound breast images into malignant, benign, and healthy categories, with a particular emphasis on minimizing false-negative outcomes. The BUSI dataset, characterized by imbalanced class distributions, was used for the breast cancer detection. The dataset was augmented to enhance feature representations using contrast-limited adaptive histogram equalization CLAHE to address the class imbalance issue, creating the BUSICL dataset. Features extracted from both datasets with the VGG16 and ResNet50 models were then classified using a support vector machine SVM. Following the results analysis, the SVM algorithms cost matrix values were adjusted according to the inverse proportions of class distributions applying a cost-sensitive approach. In addition, the robustness of the proposed methodology is compared with the K-Means SMOTE algorithm. The proposed method achieved an overall accuracy of 99.36, surpassing the performance of previous comprehensive classification studies using the BUSI dataset.

 Breast cancer is one of the five most common types of cancer that occurs when breast tissue turns into a tumor and mainly affects women. Early diagnosis of the disease is crucial for the patients lifespan. However, misclassification of malignancy may result in treatment delays and initiate an irreversible process for the patient. This study proposes an approach for classifying ultrasound breast images into malignant, benign, and healthy categories, with a particular emphasis on minimizing false-negative outcomes. The BUSI dataset, characterized by imbalanced class distributions, was used for the breast cancer detection. The dataset was augmented to enhance feature representations using contrast-limited adaptive histogram equalization CLAHE to address the class imbalance issue, creating the BUSICL dataset. Features extracted from both datasets with the VGG16 and ResNet50 models were then classified using a support vector machine SVM. Following the results analysis, the SVM algorithms cost matrix values were adjusted according to the inverse proportions of class distributions applying a cost-sensitive approach. In addition, the robustness of the proposed methodology is compared with the K-Means SMOTE algorithm. The proposed method achieved an overall accuracy of 99.36, surpassing the performance of previous comprehensive classification studies using the BUSI dataset.

 Breast cancer is one of the five most common types of cancer that occurs when breast tissue turns into a tumor and mainly affects women. Early diagnosis of the disease is crucial for the patients lifespan. However, misclassification of malignancy may result in treatment delays and initiate an irreversible process for the patient. This study proposes an approach for classifying ultrasound breast images into malignant, benign, and healthy categories, with a particular emphasis on minimizing false-negative outcomes. The BUSI dataset, characterized by imbalanced class distributions, was used for the breast cancer detection. The dataset was augmented to enhance feature representations using contrast-limited adaptive histogram equalization CLAHE to address the class imbalance issue, creating the BUSICL dataset. Features extracted from both datasets with the VGG16 and ResNet50 models were then classified using a support vector machine SVM. Following the results analysis, the SVM algorithms cost matrix values were adjusted according to the inverse proportions of class distributions applying a cost-sensitive approach. In addition, the robustness of the proposed methodology is compared with the K-Means SMOTE algorithm. The proposed method achieved an overall accuracy of 99.36, surpassing the performance of previous comprehensive classification studies using the BUSI dataset.

Makale detayları

Breast Cancer Classification in Ultrasound Imaging Using Cost-Sensitive Learning and K-Means SMOTE on the Imbalanced BUSI Dataset with Deep Feature Extraction

Yazarlar:

Nedim Muzoglu

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Life and Complex Adaptive Systems

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 2, pp.755-776

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351273

pISSN:

2147-3129

Breast cancer

Clahe

Cost-sensitive learning

K-means smote

Transfer learning.

Sayı

Makale detayları

Breast Cancer Classification in Ultrasound Imaging Using Cost-Sensitive Learning and K-Means SMOTE on the Imbalanced BUSI Dataset with Deep Feature Extraction

Yazarlar: Nedim Muzoglu

Yayıncı: Bitlis Eren University

Ana Başlıklar: Artificial Life and Complex Adaptive Systems

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 2, pp.755-776

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001351273

pISSN: 2147-3129

Breast cancer

Clahe

Cost-sensitive learning

K-means smote

Transfer learning.

Sayı

Yazarlar:

Nedim Muzoglu

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Life and Complex Adaptive Systems

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.14 Issue 2, pp.755-776

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351273

pISSN:

2147-3129