Deep Convolution Neural Networks Learned Image Classification for Early Cancer Detection Using Lightweight

Abstract

Cancer is uncontrolled cell growth in any part of the body. Early cancer detection aims to identify patients who exhibit symptoms early on in order to maximise their chances of a successful treatment. Cancer disease mortality is decreased through early detection and treatment. Numerous researchers proposed a variety of image processing and machine learning approaches for cancer detection. However, existing systems did not improve detection accuracy or efficiency. A Deep Convolutional Neural Learning Classifier Model based on the Least Mean Square Filterative Ricker Wavelet Transform (L-DCNLC) is proposed to address the aforementioned issues. The L-DCNLC Model's primary objective is to detect cancer earlier by utilising a fully connected max pooling deep convolutional network with increased accuracy and reduced time consumption. The fully connected max pooling deep convolutional network is composed of one input layer, three hidden layers, and one output layer. Initially, the input layer of the L-DCNLC Model considers the number of patient images in the database as input.