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Early Detection of Cancer

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Brain tumor detection and multi-classification using advanced deep learning techniques.

Microscopy research and technique
A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, a...

Learning Spatiotemporal Features for Esophageal Abnormality Detection From Endoscopic Videos.

IEEE journal of biomedical and health informatics
Esophageal cancer is categorized as a type of disease with a high mortality rate. Early detection of esophageal abnormalities (i.e. precancerous and early cancerous) can improve the survival rate of the patients. Recent deep learning-based methods fo...

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies.

BMC medicine
BACKGROUND: Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colp...

Noninvasive Precision Screening of Prostate Cancer by Urinary Multimarker Sensor and Artificial Intelligence Analysis.

ACS nano
Screening for prostate cancer relies on the serum prostate-specific antigen test, which provides a high rate of false positives (80%). This results in a large number of unnecessary biopsies and subsequent overtreatment. Considering the frequency of t...

MD-NDNet: a multi-dimensional convolutional neural network for false-positive reduction in pulmonary nodule detection.

Physics in medicine and biology
Pulmonary nodule false-positive reduction is of great significance for automated nodule detection in clinical diagnosis of low-dose computed tomography (LDCT) lung cancer screening. Due to individual intra-nodule variations and visual similarities be...

Multi-path synergic fusion deep neural network framework for breast mass classification using digital breast tomosynthesis.

Physics in medicine and biology
OBJECTIVE: To develop and evaluate a multi-path synergic fusion (MSF) deep neural network model for breast mass classification using digital breast tomosynthesis (DBT).