AIMC Topic: Female

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Breast cancer detection using deep learning: Datasets, methods, and challenges ahead.

Computers in biology and medicine
Breast Cancer (BC) is the most commonly diagnosed cancer and second leading cause of mortality among women. About 1 in 8 US women (about 13%) will develop invasive BC throughout their lifetime. Early detection of this life-threatening disease not onl...

Classification of nine types of pornographic materials using the sAI 0.3 model. Continuation of the pilot study.

Psychiatria polska
OBJECTIVES: Legal pornographic materials are a heterogenous group of audiovisual materials that depict one or more person over the age of eighteen engaging in sexual activities. The aim of this study was to train a model that could classify given typ...

Cloud Computing-Based Framework for Breast Tumor Image Classification Using Fusion of AlexNet and GLCM Texture Features with Ensemble Multi-Kernel Support Vector Machine (MK-SVM).

Computational intelligence and neuroscience
Breast cancer is common among women all over the world. Early identification of breast cancer lowers death rates. However, it is difficult to determine whether these are cancerous or noncancerous lesions due to their inconsistencies in image appearan...

Physiological and perceptual consequences of trust in collaborative robots: An empirical investigation of human and robot factors.

Applied ergonomics
Measuring trust is an important element of effective human-robot collaborations (HRCs). It has largely relied on subjective responses and thus cannot be readily used for adapting robots in shared operations, particularly in shared-space manufacturing...

Application of Deep Learning to Reduce the Rate of Malignancy Among BI-RADS 4A Breast Lesions Based on Ultrasonography.

Ultrasound in medicine & biology
The aim of the work described here was to develop an ultrasound (US) image-based deep learning model to reduce the rate of malignancy among breast lesions diagnosed as category 4A of the Breast Imaging-Reporting and Data System (BI-RADS) during the p...

Lightweight Deep Learning Classification Model for Identifying Low-Resolution CT Images of Lung Cancer.

Computational intelligence and neuroscience
With an astounding five million fatal cases every year, lung cancer is among the leading causes of mortality worldwide for both men and women. The diagnosis of lung illnesses can benefit from the information a computed tomography (CT) scan can offer....

Application of Feature Selection and Deep Learning for Cancer Prediction Using DNA Methylation Markers.

Genes
DNA methylation is a process that can affect gene accessibility and therefore gene expression. In this study, a machine learning pipeline is proposed for the prediction of breast cancer and the identification of significant genes that contribute to t...

Analysis of the Impact of the Development Level of Aerobics Movement on the Public Health of the Whole Population Based on Artificial Intelligence Technology.

Journal of environmental and public health
With the enhancement of China's comprehensive national power and the improvement of people's living standards, health has become the goal that people pursue. While people are thirsty for extensive knowledge and a healthy body, they also pay more atte...

Use of Deep Learning to Detect the Maternal Heart Rate and False Signals on Fetal Heart Rate Recordings.

Biosensors
We have developed deep learning models for automatic identification of the maternal heart rate (MHR) and, more generally, false signals (FSs) on fetal heart rate (FHR) recordings. The models can be used to preprocess FHR data prior to automated analy...

A novel machine learning model for class III surgery decision.

Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie
PURPOSE: The primary purpose of this study was to develop a new machine learning model for the surgery/non-surgery decision in class III patients and evaluate the validity and reliability of this model.