Author(s) :
S. Karthick
Article Name :
An Efficient Cervical Cancer Prediction Using Chicken Swarm Optimized ANN Classifier
Abstract :
Cervical cancer remains a significant global health challenge, ranking as a leading cause of cancer related deaths among women. This research addresses the need for improved detection methods by leveraging advanced image processing techniques. Implement Adaptive Median Filtering (AMF) for effective noise reduction while preserving critical details in cervical images, enhancing overall image quality. Utilizing K means clustering for segmentation, accurately isolate regions of interest, and Local Binary Patterns (LBP) for feature extraction enable improved differentiation between normal and cancerous tissues. Additionally, the integration of the Chicken Swarm Optimized algorithm with Artificial Neural Networks (CSOANN) significantly enhances classification accuracy, facilitating earlier detection of cervical cancer. These methodologies not only improve diagnostic accuracy but also contribute to better patient outcomes and more effective screening strategies, ultimately advancing cervical cancer management.
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