Lung Cancer Detection Based on Kernel PCA-Convolution Neural Network Feature Extraction and Classification by Fast Deep Belief Neural Network in Disease Management Using Multimedia Data Sources.

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Deepak Kumar Jain, Kesana Mohana Lakshmi, Kothapalli Phani Varma, Manikandan Ramachandran, Subrato Bharati

Author Information

Deepak Kumar Jain: Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing, College of Automation, Chongqing University of Posts and Telecommunications, Chongqing, China.
Kesana Mohana Lakshmi: Department of Electronics and Communication Engineering, CMR Technical Campus, Kandlakoya, Secunderabad 501401, Telangana, India.
Kothapalli Phani Varma: Department of Electronics and Communication Engineering, Sagi Rama Krishnam Raju Engineering College, Bhimavaram 534204, Andhra Pradesh, India.
Manikandan Ramachandran: School of Computing, SASTRA Deemed University, Thanjavur, India.
Subrato Bharati: Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka 1205, Bangladesh. ORCID

PMID: 35669646 DOI: 10.1155/2022/3149406

In lung cancer, tumor histology is a significant predictor of treatment response and prognosis. Although tissue samples for pathologist view are the most pertinent approach for histology classification, current advances in DL for medical image analysis point to the importance of radiologic data in further characterization of disease characteristics as well as risk stratification. Cancer is a complex global health problem that has seen an increase in death rates in recent years. Progress in cancer disease detection based on subset traits has enabled awareness of significant as well as exact disease diagnosis, thanks to the rapid flowering of high-throughput technology as well as numerous ML techniques that have emerged in recent years. As a result, advanced ML approaches that can successfully distinguish lung cancer patients from healthy people are of major importance. This paper proposed lung tumor detection based on histopathological image analysis using deep learning architectures. Here, the input image is taken as a histopathological image, and it has also been processed for removing noise, image resizing, and enhancing the image. Then the image features are extracted using Kernel PCA integrated with a convolutional neural network (KPCA-CNN), in which KPCA has been used in the feature extraction layer of CNN. The classification of extracted features has been put into effect using a Fast Deep Belief Neural Network (FDBNN). Finally, the classified output will give the tumorous cell and nontumorous cell of the lung from the input histopathological image. The experimental analysis has been carried out for various histopathological image datasets, and the obtained parameters are accuracy, precision, recall, and F-measure. Confusion matrix gives the actual class and predicted class of tumor in an input image. From the comparative analysis, the proposed technique obtains enhanced output in detecting the tumor once compared with an existing methodology for the various datasets.

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Disease Management

Humans

Information Storage and Retrieval

Lung

Lung Neoplasms

Multimedia

Neural Networks, Computer

Journal Article

OpenLB
Open Library of Bioscience