Automatic classification of cervical cancer from cytological images by using convolutional neural network

M Wu, C Yan, H Liu, Q Liu, Y Yin - Bioscience reports, 2018 - portlandpress.com
Cervical cancer (CC) is one of the most common gynecologic malignancies in the world. The
incidence and mortality keep high in some remote and poor medical condition regions in …

Breast cancer identification via thermography image segmentation with a gradient vector flow and a convolutional neural network

S Tello-Mijares, F Woo, F Flores - Journal of healthcare …, 2019 - Wiley Online Library
Breast cancer is the most common cancer among women worldwide with about half a million
cases reported each year. Mammary thermography can offer early diagnosis at low cost if …

Breast histopathological image classification method based on autoencoder and siamese framework

M Liu, Y He, M Wu, C Zeng - Information, 2022 - mdpi.com
The automated classification of breast cancer histopathological images is one of the
important tasks in computer-aided diagnosis systems (CADs). Due to the characteristics of …

[HTML][HTML] Feature selection and classification in mammography using hybrid crow search algorithm with Harris hawks optimization

S Thawkar - Biocybernetics and Biomedical Engineering, 2022 - Elsevier
The purpose of this study is to develop a hybrid algorithm for feature selection and
classification of masses in digital mammograms based on the Crow search algorithm (CSA) …

Breast cancer: A hybrid method for feature selection and classification in digital mammography

S Thawkar, V Katta, AR Parashar… - … Journal of Imaging …, 2023 - Wiley Online Library
In this article, a hybrid approach based on the Whale optimization algorithm (WOA) and the
Dragonfly algorithm (DA) is proposed for breast cancer diagnosis. The hybrid WOADA …

[HTML][HTML] Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms

S Sapate, S Talbar, A Mahajan, N Sable… - Biocybernetics and …, 2020 - Elsevier
Ipsilateral views of digital mammograms help radiologists to localize and confirm abnormal
lesions during diagnosis of breast cancers. This study aims at developing algorithms which …

Machine learning and new insights for breast cancer diagnosis

Y Guo, H Zhang, L Yuan, W Chen… - Journal of …, 2024 - journals.sagepub.com
Breast cancer (BC) is the most prominent form of cancer among females all over the world.
The current methods of BC detection include X-ray mammography, ultrasound, computed …

[HTML][HTML] Multi-scale attention-guided network for mammograms classification

C Xu, M Lou, Y Qi, Y Wang, J Pi, Y Ma - Biomedical Signal Processing and …, 2021 - Elsevier
For the breast mass segmentation in whole mammograms, in our studies, we observe that
there is an enormous performance reduction in the case of considering the normal data …

[HTML][HTML] An intelligent healthcare system for optimized breast cancer diagnosis using harmony search and simulated annealing (HS-SA) algorithm

TA Shaikh, R Ali - Informatics in Medicine Unlocked, 2020 - Elsevier
The paper offers a crossbreed streamlining algorithm combining harmony search (HS) and
simulated annealing (SA) known as harmony search and simulated annealing (HS-SA) for …

In-vehicle alcohol detection using low-cost sensors and genetic algorithms to aid in the drinking and driving detection

JM Celaya-Padilla, JS Romero-González… - Sensors, 2021 - mdpi.com
Worldwide, motor vehicle accidents are one of the leading causes of death, with alcohol-
related accidents playing a significant role, particularly in child death. Aiming to aid in the …