CerviCell-detector: An object detection approach for identifying the cancerous cells in pap smear images of cervical cancer.

Cervical cancer is the second most commonly seen cancer in women. It affects the cervix portion of the vagina. The most preferred diagnostic test required for screening for cervical cancer is the pap smear test.

Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min-Max Neural Network for Cervical Cancer Diagnosis.

Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error.

ColpoClassifier: A Hybrid Framework for Classification of the Cervigrams.

Colposcopy plays a vital role in detecting cervical cancer. Artificial intelligence-based methods have been implemented in the literature for the classification of colposcopy images. However, there is a need for a more effective method that can accurately classify cervigrams.

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions.

With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users' emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks.

Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.

Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization.

DeepCyto: a hybrid framework for cervical cancer classification by using deep feature fusion of cytology images.

Cervical cancer is the second most commonly seen cancer in women. It affects the cervix portion of the vagina. The most preferred diagnostic test required for screening cervical cancer is the pap smear test.

Examining sex differentials in the uptake and process of HIV testing in three high prevalence districts of India.

Sex differentials in the uptake of HIV testing have been reported in a range of settings, however, men's and women's testing patterns are not consistent across these settings, suggesting the need to set sex differentials against gender norms in patient testing behaviour and provider practices. A community-based, cross-sectional survey among 347 people living with HIV in three HIV high prevalence districts of India examined reasons for undergoing an HIV test, location of testing and conditions under which individuals were tested. HIV testing was almost always provider-initiated for men.