Breast cancer detection using infrared thermal imaging and a deep learning model
Women's breasts are susceptible to developing cancer; this is supported by a recent study
from 2016 showing that 2.8 million women worldwide had already been diagnosed with …
from 2016 showing that 2.8 million women worldwide had already been diagnosed with …
A review on image-based approaches for breast cancer detection, segmentation, and classification
Z Rezaei - Expert Systems with Applications, 2021 - Elsevier
The breast cancer as the most life-threatening disease among the woman has emerged in
the worldwide. It is supposed that the early testing and treatment for breast cancer detection …
the worldwide. It is supposed that the early testing and treatment for breast cancer detection …
Breast cancer detection using mammogram images with improved multi-fractal dimension approach and feature fusion
DA Zebari, DA Ibrahim, DQ Zeebaree… - Applied Sciences, 2021 - mdpi.com
Breast cancer detection using mammogram images at an early stage is an important step in
disease diagnostics. We propose a new method for the classification of benign or malignant …
disease diagnostics. We propose a new method for the classification of benign or malignant …
Automated breast cancer detection in digital mammograms: A moth flame optimization based ELM approach
Early detection of breast cancer based on a digital mammogram is an important research
domain in the field of medical image analysis. An improved CAD model is proposed in this …
domain in the field of medical image analysis. An improved CAD model is proposed in this …
Breast cancer detection in mammography images: a CNN-based approach with feature selection
The prompt and accurate diagnosis of breast lesions, including the distinction between
cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast …
cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast …
[HTML][HTML] Feature fusion and Ensemble learning-based CNN model for mammographic image classification
In recent times, the world has faced an alarming situation regarding breast cancer patients.
The early diagnosis of this deadly disease can make the treatment more accessible and …
The early diagnosis of this deadly disease can make the treatment more accessible and …
Multi-view mammographic density classification by dilated and attention-guided residual learning
Breast density is widely adopted to reflect the likelihood of early breast cancer development.
Existing methods of mammographic density classification either require steps of manual …
Existing methods of mammographic density classification either require steps of manual …
Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network
This paper presents a method for automatic breast pectoral muscle segmentation in
mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired …
mediolateral oblique mammograms using a Convolutional Neural Network (CNN) inspired …
An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine
Over the past years, the surge in the necessity for early detection and diagnosis of breast
cancer has resulted in many innovative research directions. According to the World Health …
cancer has resulted in many innovative research directions. According to the World Health …
Mammographic classification based on XGBoost and DCNN with multi features
R Song, T Li, Y Wang - IEEE Access, 2020 - ieeexplore.ieee.org
The classification of benign and malignant masses in mammograms by Computer-Aided
Diagnosis (CAD) is one of the most difficult and important tasks in the development of CAD …
Diagnosis (CAD) is one of the most difficult and important tasks in the development of CAD …