RETRACTED ARTICLE: Deep convolution neural networks learned image classification for early cancer detection using lightweight

Abstract

Essentially, cancer refers to the formation of abnormal cells in any section or area of the body. The goal of early cancer detection is to identify patients who are showing signs early on in order to maximise their chances of a successful therapy. Early detection and treatment of cancer reduce the disease mortality. A wide range of image processing and machine learning techniques have been presented for the identification of cancer. There was no improvement in detection accuracy or efficiency with existing systems. To overcome these problems, we present the Least Mean Square Filterative Ricker Wavelet Transform-based Deep Convolutional Neural Learning Classifier Model (L-DCNLC). By using a fully connected max pooling deep convolutional network with higher accuracy and reduced time consumption, the L-DCNLC Model aims to identify cancer early. There are three hidden layers and one output layer in the fully linked max pooling deep convolutional network. The number of patient photographs in the database is used as input in the L-DCNLC Model's input layer. Following that, preprocessing is performed in hidden layer 1 to perform denoising in order to improve image quality. This is accomplished by employing the Least Mean Square Weiner Filtering process, which has a higher peak signal-to-noise ratio. The Continuous Ricker Wavelet Transform is then used to extract the image features from the preprocessed image in hidden layer 2. Finally, the classification process is performed in hidden layer 3 using the Kulczynksi Similarity Coefficient to detect the cancerous image by comparing the testing and extracted features. Thus, the cancerous image is detected and displayed with a low error rate in the output layer. The brain cancer and lung cancer datasets are analysed to determine peak signal to noise ratios (PSNR), cancer detection accuracy, and cancer detection time. The evaluation results indicate that the L-DCNLC Model improves accuracy and PSNR while requiring less computational time than previous works.