Deep convolutional neural networks based cervical cancer detection and classification

Cervical cancer (CC) is a major reason of death in cancer in women. The problem of this cancer is limited once it can be analysed and preserved at the initial phase. With the progress of artificial intelligence (AI) technology, computer aided diagnosis (CAD) is developed most leading investigation topics of medicinal imaging during recent decades. This study develops a CAD Model for Cervical Cancer Classification using Deep Learning (CADC3-DL) model. The presented CADC3-DL model aims to recognise the occurrence of CC on biomedical images. Initially, the CADC3-DL technique creates a mask from the input dataset. Next, the pre-processing step takes place in two levels namely Gaussian filtering (GF) based noise removal and CLAHE based contrast enhancement. Then, the CADC3-DL technique employs customised U-Net segmentation technique where the filter size in the traditional U-Net is replaced by batch normalisation (BN) to accomplish enhanced classification accuracy.