AIMC Topic: Papanicolaou Test

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Enhancing pap smear image classification: integrating transfer learning and attention mechanisms for improved detection of cervical abnormalities.

Biomedical physics & engineering express
Cervical cancer remains a major global health challenge, accounting for significant morbidity and mortality among women. Early detection through screening, such as Pap smear tests, is crucial for effective treatment and improved patient outcomes. How...

CINNAMON-GUI: Revolutionizing Pap Smear Analysis with CNN-Based Digital Pathology Image Classification.

F1000Research
BACKGROUND: Medical imaging has seen significant advancements through machine learning, particularly convolutional neural networks (CNNs). These technologies have transformed the analysis of pathological images, enhancing the accuracy of diagnosing a...

A two-stream decision fusion network for cervical pap-smear image classification tasks.

Tissue & cell
Deep learning, especially Convolution Neural Networks (CNNs), has demonstrated superior performance in image recognition and classification tasks. They make complex pattern recognition possible by extracting image features through layers of abstracti...

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.

Scientific reports
Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of variou...

Assessment of Efficacy and Accuracy of Cervical Cytology Screening With Artificial Intelligence Assistive System.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
The role of artificial intelligence (AI) in pathology offers many exciting new possibilities for improving patient care. This study contributes to this development by identifying the viability of the AICyte assistive system for cervical screening, an...

Improving cervical cancer classification in PAP smear images with enhanced segmentation and deep progressive learning-based techniques.

Diagnostic cytopathology
OBJECTIVE: Cervical cancer, a prevalent and deadly disease among women, comes second only to breast cancer, with over 700 daily deaths. The Pap smear test is a widely utilized screening method for detecting cervical cancer in its early stages. Howeve...

Cervical cell's nucleus segmentation through an improved UNet architecture.

PloS one
Precise segmentation of the nucleus is vital for computer-aided diagnosis (CAD) in cervical cytology. Automated delineation of the cervical nucleus has notorious challenges due to clumped cells, color variation, noise, and fuzzy boundaries. Due to it...

Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning.

Scientific reports
Every year cervical cancer affects more than 300,000 people, and on average one woman is diagnosed with cervical cancer every minute. Early diagnosis and classification of cervical lesions greatly boosts up the chance of successful treatments of pati...

A fuzzy rank-based ensemble of CNN models for classification of cervical cytology.

Scientific reports
Cervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime importance for eradicating the disease from the patient's body. However, regular population-wise sc...

Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting.

JAMA network open
IMPORTANCE: Cervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programs. The creation of a diagnostic system to digitize Papanicolaou test samples and analyze them using a cloud-based deep learning...