Deep learning neural network for lung cancer classification: enhanced optimization function

Abstract

Background and Purpose: Convolutional neural network is widely used for image recognition in the medical area at nowadays. However, overall accuracy in predicting lung tumor is low and the processing time is high as the error occurred while reconstructing the CT image. The aim of this work is to increase the overall prediction accuracy along with reducing processing time by using multispace image in pooling layer of convolution neural network. Methodology: The proposed method has the autoencoder system to improve the overall accuracy, and to predict lung cancer by using multispace image in pooling layer of convolution neural network and Adam Algorithm for optimization. First, the CT images were pre-processed by feeding image to the convolution filter and down sampled by using max pooling. Then, features are extracted using the autoencoder model based on convolutional neural network and multispace image reconstruction technique is used to reduce error while reconstructing the image which then results improved accuracy to predict lung nodule. Finally, the reconstructed images are taken as input for SoftMax classifier to classify the CT images. Results: The state-of-art and proposed solutions were processed in Python Tensor Flow and It provides significant increase in accuracy in classification of lung cancer to 99.5 from 98.9 and decrease in processing time from 10 frames/second to 12 seconds/second. Conclusion: The proposed solution provides high classification accuracy along with less processing time compared to the state of art. For future research, large dataset can be implemented, and low pixel image can be processed to evaluate the classification.

A multichannel analysis of imbalanced computed tomography data for lung cancer classification

“…These studies suggest that the automatic detection of different diseases is possible with the help of real-time CAD systems. Similarly, several studies have been conducted for detecting lung cancer using computer algorithms [4,[14][15][16]. In this regard, a CAD system that uses CT scan data to identify tumors in their early stages is proposed in [14].…”

“…Other evaluation matrices were also considered, for instance, the false positive rate (FPR), false negative rate (FNR), and ROC curve to demonstrate the superiority of the designed model. In [16], an autoencoder based system was designed using multispace images in the pooling layer of a CNN to identify lung cancer. In order to enhance the overall prediction accuracy, the Adam optimizer was used to optimize the network.…”

Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours

“…The automatic segmentation approach hugely reduces an expert’s image analysing time (per patient, a CT scan contains 100s of image slices), minimizes the false-positive rate, improves segmentation accuracy, aids in achieving precise classification results, and improves 3D visualization quality. Deep learning (DL) focuses not only on the depth of the learning model but also on the prominence of feature-learning facilitated over the network model [ 11 ]. It has established its stand in the research of NSCLC detection.…”