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Table 1 Some of the available techniques in the literature for automated/semi-automated detection of cervical cancer from pap-smear images

From: A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

Author

Paper

Datasets

Features

Pre-processing

Segmentation

Classification

Results

Su et al. [19]

Automatic detection of cervical cancer cells by a two-level cascade classification system

Liquid-based cytology slides

20 Morphological and 8 texture features

Histogram equalization and Median filter

Adaptive threshold

C4.5 and Logical Regression classifiers

Recognition rates of 95.6% achieved

Sharma et al. [20]

Classification of clinical dataset of cervical cancer using KNN

Single cells data sets from Fortis Hospital, India

7 morphological features

Gaussian filter and histogram equalization

Min–max and edge detection

K-nearest neighbour

Accuracy of 82.9% with fivefold cross-validation

Kumar et al. [21]

Detection and classification of cancer from microscopic biopsy images using clinically significant features

Histology image dataset (histology DS2828)

125 Nucleus and cytoplasm morphologic features

Contrast limited adaptive histogram equalization

K-means segmentation algorithm

K-NN, fuzzy KNN, SVM and random forest-based classifiers

Accuracy, specificity and sensitivity of 92%, 94% and 81%

Chankong et al. [22]

Automatic cervical cell segmentation and classification in Pap smears

Herlev dataset

Morphological features

Median filter

Patch-based fuzzy C-means and FCM

Fuzzy C-means

Accuracies of 93.78% and 99.27% for 7 and 2-class classifications

Talukdar et al. [23]

Fuzzy clustering based image segmentation of pap smear images of cervical cancer cell using FCM algorithm

Colour image

Morphometric, densitometry, colorimetric and textural feature

Adaptive histogram equalization with Otsu’s method

Chaos theory corresponding to R, G and B value

Pixel-level classification and shape analysis

Preserves the colour of the images and data loss is minimal

Sreedevi et al. [24]

Pap smear image-based detection of cervical cancer,

Herlev dataset

Nucleus features

Colour conversions and contrast enhancement

Iterative thresholding method

Based on the area of the nucleus

A sensitivity of 100% and specificity of 90% achieved

Ampazis et al. [25]

Pap-smear classification using efficient second-order neural network

Herlev University Hospital

20 morphological features

Contrast enhancement

Neural networks

LMAM and OLMAM algorithms

Classification accuracy of 98.86% was obtained