Cervical cell classification plays a key role in the computer-based screening and diagnosis. This article focuses on cell classification and grading to differentiate the stages of cervical dysplasia. The proposed framework uses a unification of wavelet transform and convolutional neural network (CNN) for segregating spectral and spatial features from papanicolaou stained (pap) smear images. A correlation-based feature selection is adopted to find relevant features from the CNN model. Random Forest classifier is then incorporated
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