Medical image identification methods: A review

J Li, P Jiang, Q An, GG Wang, HF Kong - Computers in Biology and …, 2024 - Elsevier
The identification of medical images is an essential task in computer-aided diagnosis,
medical image retrieval and mining. Medical image data mainly include electronic health …

[HTML][HTML] Breast cancer diagnosis: A systematic review

X Wen, X Guo, S Wang, Z Lu, Y Zhang - Biocybernetics and Biomedical …, 2024 - Elsevier
The second-leading cause of death for women is breast cancer. Consequently, a precise
early diagnosis is essential. With the rapid development of artificial intelligence, computer …

Deep learning empowered breast cancer diagnosis: Advancements in detection and classification

J Ahmad, S Akram, A Jaffar, Z Ali, SM Bhatti, A Ahmad… - Plos one, 2024 - journals.plos.org
Recent advancements in AI, driven by big data technologies, have reshaped various
industries, with a strong focus on data-driven approaches. This has resulted in remarkable …

[HTML][HTML] A novel fusion framework of deep bottleneck residual convolutional neural network for breast cancer classification from mammogram images

K Jabeen, MA Khan, MA Hameed, O Alqahtani… - Frontiers in …, 2024 - frontiersin.org
With over 2.1 million new cases of breast cancer diagnosed annually, the incidence and
mortality rate of this disease pose severe global health issues for women. Identifying the …

Deep learning performance for detection and classification of microcalcifications on mammography

F Pesapane, C Trentin, F Ferrari, G Signorelli… - European Radiology …, 2023 - Springer
Background Breast cancer screening through mammography is crucial for early detection,
yet the demand for mammography services surpasses the capacity of radiologists. Artificial …

[HTML][HTML] MFAN: multi-feature attention network for breast cancer classification

IM Nasir, MA Alrasheedi, NA Alreshidi - Mathematics, 2024 - mdpi.com
Cancer-related diseases are some of the major health hazards affecting individuals globally,
especially breast cancer. Cases of breast cancer among women persist, and the early …

Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study

Y Shi, LT Olsson, KA Hoadley, BC Calhoun… - NPJ Breast …, 2023 - nature.com
Approaches for rapidly identifying patients at high risk of early breast cancer recurrence are
needed. Image-based methods for prescreening hematoxylin and eosin (H&E) stained …

[HTML][HTML] Grad-CAM Enabled Breast Cancer Classification with a 3D Inception-ResNet V2: Empowering Radiologists with Explainable Insights

FM Talaat, SA Gamel, RM El-Balka, M Shehata… - Cancers, 2024 - mdpi.com
Breast cancer (BCa) poses a severe threat to women's health worldwide as it is the most
frequently diagnosed type of cancer and the primary cause of death for female patients. The …

Machine learning applications in breast cancer prediction using mammography

GM Harshvardhan, K Mori, S Verma… - Image and Vision …, 2024 - Elsevier
Breast cancer is the second leading cause of cancer-related deaths among women. Early
detection of lumps and subsequent risk assessment significantly improves prognosis. In …

Segmentation for mammography classification utilizing deep convolutional neural network

D Kumar Saha, T Hossain, M Safran, S Alfarhood… - BMC Medical …, 2024 - Springer
Background Mammography for the diagnosis of early breast cancer (BC) relies heavily on
the identification of breast masses. However, in the early stages, it might be challenging to …