Impact of Variation in Number of Channels in CNN Classification model for Cervical Cancer Detection

Abstract

Development in Computer vision applications, most prominently in Artificial Intelligence (AI) over the past decade, has encouraged researchers to use computational algorithms. Machine Learning (ML) and Deep Learning (DL) algorithms are getting attention in the biomedical area to early diagnosis and prognosis of lethiferous diseases with higher accuracy using statistical analysis. Cervical cancer is one of the predominant gynecological infectious forms of cancer in females ecumenically, generally transpiring in less-developed nations. DL algorithms are more optimized and convenient due to the automatic feature extraction in Cervical lesion detection at an early phase. Here we analyze the convolutional neural network (CNN) models' performance using variation in channels depth comprises in the convolution layers to classify multi-class liquid-based cytology (LBC) Whole slide images (WSI). We propose three CNN models having two convolution layers with the number of channels (4,8), (8,16), and (32,64) and two pooling layers. CNN model with the highest number of channels in the convolution layers gives the leading performance with an accuracy of 96.89%, precision of 93.38%, the sensitivity of 93.75%, and F-score of 94.15%.