Detection and classification of lung cancer using deep neural network
subject: DCNN; deep convolution neural network; EfficientNetB7; histopathology; lung cancer
description: Lung cancers hold a critical spot among the reasons for most cancer deaths among humans. The better way to maximise the survival rate is the detection of cancer at the earliest. But existing traditional techniques are time-consuming and error-prone. This study is a significant and efficient method for the detection and classification of lung cancer into large cell carcinomas, small cell, adenocarcinoma, squamous cell carcinomas, or benign respectively. In the proposed technique, a novel algorithm is implemented to generate the Image patches from whole slide histopathological images. Then, histogram normalisation is carried out to remove noise and enhance the image. Then a novel extended Mobius transformation technique is used for image augmentation. Finally, Dense EfficientNetB7 is trained to extract the features for the detection and classification of lung cancer. The performance of the proposed technique is proved more efficient and par with histologists attaining an accuracy of 98.87%. Copyright 2025 Inderscience Enterprises Ltd.
creator: Kumar, S. Babu; Kumar, M. Vinoth
source: International Journal of Medical Engineering and Informatics;Volume;17;Issue;5;pp.429-443
publisher: Inderscience Publishers
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 17550653;
format: online
language: English
type: Article
coverage: Kumar S.B., Department of AI, ML & DS, CHRIST University, Karnataka, Bangalore, India; Kumar M.V., Department of Information Science and Engineering, RV Institute of Technology and Management, Karnataka, Bangalore, India, Visvesvaraya Technological University, Karnataka, Belagavi, 590018, India