1 research outputs found
Algorithms for screening of Cervical Cancer: A chronological review
There are various algorithms and methodologies used for automated screening
of cervical cancer by segmenting and classifying cervical cancer cells into
different categories. This study presents a critical review of different
research papers published that integrated AI methods in screening cervical
cancer via different approaches analyzed in terms of typical metrics like
dataset size, drawbacks, accuracy etc. An attempt has been made to furnish the
reader with an insight of Machine Learning algorithms like SVM (Support Vector
Machines), GLCM (Gray Level Co-occurrence Matrix), k-NN (k-Nearest Neighbours),
MARS (Multivariate Adaptive Regression Splines), CNNs (Convolutional Neural
Networks), spatial fuzzy clustering algorithms, PNNs (Probabilistic Neural
Networks), Genetic Algorithm, RFT (Random Forest Trees), C5.0, CART
(Classification and Regression Trees) and Hierarchical clustering algorithm for
feature extraction, cell segmentation and classification. This paper also
covers the publicly available datasets related to cervical cancer. It presents
a holistic review on the computational methods that have evolved over the
period of time, in chronological order in detection of malignant cells.Comment: This critical review of various machine learning algorithms for
Cervical Cancer Screening was completed at National Institute of
Biologicals(NIB), India by B.Tech final year Computer Science students at
JSSATE, Noida, India under the supervision of Director at NIB Dr. Surinder
Singh and Jr. Scientist Sh. P.S. Chandranan