Cervical Cancer Screening

Cervical cancer screening aims to detect precancerous and cancerous lesions early, improving treatment outcomes and survival rates. Current research heavily emphasizes the application of artificial intelligence, particularly deep convolutional neural networks (CNNs) and variations like multi-branch CNNs and multi-structural CNNs, to analyze cervical cell images from Pap smears and VIA (Visual Inspection with Acetic Acid) procedures, often achieving accuracy comparable to or exceeding human experts. This focus on AI-assisted diagnosis addresses challenges like pathologist workload and variability in interpretation, potentially leading to more efficient and accurate screening, especially in resource-limited settings. The development of robust, interpretable AI models that can handle data imbalances and real-world variations in image quality is a key area of ongoing investigation.