(KAUH-BCMD) dataset: advancing mammographic breast cancer classification with multi-fusion preprocessing and residual depth-wise network DOI Creative Commons
Asma’a Al-Mnayyis, Hasan Gharaibeh,

Mohammad Amin

et al.

Frontiers in Big Data, Journal Year: 2025, Volume and Issue: 8

Published: March 6, 2025

The categorization of benign and malignant patterns in digital mammography is a critical step the diagnosis breast cancer, facilitating early detection potentially saving many lives. Diverse tissue architectures often obscure conceal issues. Classifying worrying regions (benign patterns) mammograms significant challenge for radiologists. Even specialists, first visual indicators are nuanced irregular, complicating identification. Therefore, radiologists want an advanced classifier to assist identifying cancer categorizing concern. This study presents enhanced technique classification using images. collection comprises real-world data from King Abdullah University Hospital (KAUH) at Jordan Science Technology, consisting 7,205 photographs 5,000 patients aged 18-75. After being classified as or malignant, pictures underwent preprocessing by rescaling, normalization, augmentation. Multi-fusion approaches, such high-boost filtering contrast-limited adaptive histogram equalization (CLAHE), were used improve picture quality. We created unique Residual Depth-wise Network (RDN) enhance precision detection. suggested RDN model was compared with prominent models, including MobileNetV2, VGG16, VGG19, ResNet50, InceptionV3, Xception, DenseNet121. exhibited superior performance, achieving accuracy 97.82%, 96.55%, recall 99.19%, specificity 96.45%, F1 score 97.85%, validation 96.20%. findings indicate that proposed excellent instrument images significantly improves when integrated multi-fusion efficient approaches.

Language: Английский

Deep learning-driven prediction in healthcare systems: Applying advanced CNNs for enhanced breast cancer detection DOI
Marouene Chaieb, M. Azzouz,

Mokhles Ben Refifa

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 189, P. 109858 - 109858

Published: Feb. 27, 2025

Language: Английский

Citations

0
(KAUH-BCMD) dataset: advancing mammographic breast cancer classification with multi-fusion preprocessing and residual depth-wise network DOI Creative Commons
Asma’a Al-Mnayyis, Hasan Gharaibeh,

Mohammad Amin

et al.

Frontiers in Big Data, Journal Year: 2025, Volume and Issue: 8

Published: March 6, 2025

The categorization of benign and malignant patterns in digital mammography is a critical step the diagnosis breast cancer, facilitating early detection potentially saving many lives. Diverse tissue architectures often obscure conceal issues. Classifying worrying regions (benign patterns) mammograms significant challenge for radiologists. Even specialists, first visual indicators are nuanced irregular, complicating identification. Therefore, radiologists want an advanced classifier to assist identifying cancer categorizing concern. This study presents enhanced technique classification using images. collection comprises real-world data from King Abdullah University Hospital (KAUH) at Jordan Science Technology, consisting 7,205 photographs 5,000 patients aged 18-75. After being classified as or malignant, pictures underwent preprocessing by rescaling, normalization, augmentation. Multi-fusion approaches, such high-boost filtering contrast-limited adaptive histogram equalization (CLAHE), were used improve picture quality. We created unique Residual Depth-wise Network (RDN) enhance precision detection. suggested RDN model was compared with prominent models, including MobileNetV2, VGG16, VGG19, ResNet50, InceptionV3, Xception, DenseNet121. exhibited superior performance, achieving accuracy 97.82%, 96.55%, recall 99.19%, specificity 96.45%, F1 score 97.85%, validation 96.20%. findings indicate that proposed excellent instrument images significantly improves when integrated multi-fusion efficient approaches.

Language: Английский

Citations

0