ArticleAsian Pacific journal of cancer prevention : APJCP2018
Image Registration based Cervical Cancer Detection and Segmentation Using ANFIS Classifier
Article in Asian Pacific journal of cancer prevention : APJCP, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Exploration of Cervical Cancer Image Processing and Detection Based on URCNNs.Current medical imaging · 2025Article
- Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders.Scientific reports · 2024Article
- Optical imaging for early detection of cervical cancer: state of the art and perspectives.Journal of biomedical optics · 2023Review
- Optimised feature selection-driven convolutional neural network using gray level co-occurrence matrix for detection of cervical cancer.Open life sciences · 2023Article
- An emerging paradigms on cervical cancer screening methods and devices for clinical trails.Frontiers in public health · 2022Article
- ANFIS-Net for automatic detection of COVID-19.Scientific reports · 2021Article
- Progress of Artificial Intelligence in Gynecological Malignant Tumors.Cancer management and research · 2020Review
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2 authors.
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Abstract
Cervical cancer is the leading cancer in women around the world. In this paper, Adaptive Neuro Fuzzy Inference System (ANFIS) classifier based cervical cancer detection and segmentation methodology is proposed. This proposed system consists of the following stages as Image Registration, Feature extraction, Classifications and Segmentation. Fast Fourier Transform (FFT) is used for image registration. Then, Grey Level Co-occurrence Matrix (GLCM), Grey level and trinary features are extracted from the registered cervical image. Next, these extracted features are trained and classified using ANFIS classifier. Morphological operations are now applied over the classified cervical image to detect and segment the cancer region in cervical images. Simulations on large cervical image dataset demonstrate that the proposed cervical cancer detection and segmentation methodology outperforms the state of-the-art methods in terms of sensitivity, specificity and accuracy.
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