Year: 2025
Type: article
Abstract: Cervical cancer is still a public health problem, making timely and accurate diagnosis crucial to improve treatment options and decrease the rates of mortality. Here, we proposed the improvement of the accuracy of detection in cervical cancer using image processing and machine learning algorithms. The research investigates innovative methods in imaging evaluation during colposcopic inspection by segmenting, extracting, and classifying cervical tissues to distinguish normal tissues from cervical cancer tissues. Overall, incorporating machine learning models into the diagnostic pipeline holds promise for substantially enhancing the reliability and accuracy of cervical cancer screening. Recent technological advancements in imaging modalities, associated with convenient data analysis algorithms, represent unique opportunities to detect even minor stages of cancer by offering better patient management options, thereby improving public health by reducing their statistical significance to society in terms of global financial burden.
Source: Cuestiones de Fisioterapia
DOI: 10.48047/q0q4yd45
Authors: Manjusha Borse, Mukesh Yadav
Institution: SAGE University Indore
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407822437