A systematic literature review of breast cancer diagnosis using machine intelligence techniques

V Nemade, S Pathak, AK Dubey - Archives of Computational Methods in …, 2022 - Springer
Breast cancer is one of the most common diseases in women; it can have long-term
implications and can even be fatal. However, early detection, achieved through recent …

Semantic segmentation of breast cancer images using DenseNet with proposed PSPNet

S Samudrala, CK Mohan - Multimedia Tools and Applications, 2024 - Springer
For early detection of cancer tumors, the semantic segmentation based technique is
proposed because the existing numerous methods fail while classifying due to accuracy and …

YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms

Y Su, Q Liu, W **e, P Hu - Computer Methods and Programs in …, 2022 - Elsevier
Background and objective Both mass detection and segmentation in digital mammograms
play a crucial role in early breast cancer detection and treatment. Furthermore, clinical …

Connected-UNets: a deep learning architecture for breast mass segmentation

A Baccouche, B Garcia-Zapirain, C Castillo Olea… - NPJ Breast …, 2021 - nature.com
Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious
breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic …

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 …

Computer-aided breast cancer diagnosis: Comparative analysis of breast imaging modalities and mammogram repositories

P Oza, P Sharma, S Patel… - Current Medical Imaging …, 2023 - benthamdirect.com
The accurate assessment or diagnosis of breast cancer depends on image acquisition and
image analysis and interpretation. The expert radiologist makes image interpretation, and …

A novel hybrid K-means and GMM machine learning model for breast cancer detection

PE Jebarani, N Umadevi, H Dang, M Pomplun - IEEE Access, 2021 - ieeexplore.ieee.org
Breast cancer is the second leading cause of death among a large number of women
worldwide. It may be challenging for radiologists to diagnose and treat breast cancer …

Deep integrated pipeline of segmentation guided classification of breast cancer from ultrasound images

MSK Inan, FI Alam, R Hasan - Biomedical Signal Processing and Control, 2022 - Elsevier
Breast cancer has become a symbol of tremendous concern in the modern world, as it is one
of the major causes of cancer mortality worldwide. In this regard, breast ultrasonography …

Improving breast cancer detection and diagnosis through semantic segmentation using the Unet3+ deep learning framework

T Alam, WC Shia, FR Hsu, T Hassan - Biomedicines, 2023 - mdpi.com
We present an analysis and evaluation of breast cancer detection and diagnosis using
segmentation models. We used an advanced semantic segmentation method and a deep …

[HTML][HTML] Novel insights in spatial epidemiology utilizing explainable AI (XAI) and remote sensing

A Temenos, IN Tzortzis, M Kaselimi, I Rallis… - Remote Sensing, 2022 - mdpi.com
The COVID-19 pandemic has affected many aspects of human life around the world, due to
its tremendous outcomes on public health and socio-economic activities. Policy makers …