Multimodal Feature Fusion Using Optimal Transfer Learning Approach for Lung Cancer Detection and Classification on CT Images

 

B. Karthikeyan1,*, N. Seethalakshmi2,V. Nandhini3, D. Vinoth4, P. Muthusamy5, Kiran Bellam6

 

1Department of Information  Technology, Panimalar Engineering College, Chennai, India

2 C.K. College of Engineering&Technology, Cuddalore-607003, India

3 Department of Computer Scienec and Engineering, Sona College of Technology, Salem, India;

4Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India

5Department of Cyber Security, Paavai Engineering College( Autonomous), Namakkal, India

6Associate Dean, College of Engineering, Prairie View A & M University, Texas, USA

Emails: karthikeyan.b32@gmail.com; seethan1989@gmail.com; nandinivijaykumar@sonatech.ac.in; vinothd.sse@saveetha.com; muthu.namakkal@gmail.com; kibellam@pvamu.edu

*Corresponding Author: karthikeyan.b32@gmail.com

 

Text Box: Abstract Lung cancer detection is the process of detecting the presence of lung tumor or abnormalities in the lungs. Early diagnosis is crucial for increasing the chances of patient survival and successful treatment. When compared to X-rays, Computed Tomography (CT) images are more sensitive and are increasingly being used for the diagnosis and screening of lung tumors. They provide complete cross-sectional images of the lungs and it will even detect small lesions. AI and Machine learning (ML) approaches are most commonly employed to analyse medical images (e.g. CT scans) and detect lung cancer. This algorithm can help radiologists identify patterns indicative or subtle abnormalities of cancer. Medical diagnosis, particularly in complex diseases such as lung cancer, frequently involves ambiguity. The diagnostic system can alleviate ambiguity via cross-verifying findings from various sources by fusing multimodal features. Multimodal feature fusion using deep learning (DL) algorithm is an advanced technology that leverages the abilities of deep neural networks to combine data from three different modalities or sources for better robustness in several applications, namely natural language processing, image, and data analysis, etc. This study introduces a Multimodal Feature Fusion using an Optimal Transfer Learning Method for Lung Cancer Detection and Classification (MFFOTL-LCDC) methodology on CT images. The chief objective of the MFFOTL-LCDC methodology is to exploit the feature fusion process for the identification and classification of lung tumor. To attain this, the MFFOTL-LCDC model undergoes a multimodal feature fusion approach to derive feature vectors using 3 DL approaches such as SqueezeNet, CapsNet, and Inception v3 models. Besides, the MFFOTL-LCDC technique applies the remora optimization algorithm (ROA) for the hyperparameter choice of 3 DL models. For lung cancer recognition, the MFFOTL-LCDC algorithm exploits the deep extreme learning machine (DELM) algorithm. A series of simulations were conducted to ensure the greater lung cancer recognition outcomes of the MFFOTL-LCDC methodology. The extensive outcomes determine the improved results of the MFFOTL-LCDC technique over recent DL approaches.  Received: August 26, 2023  Revised: November 19, 2023  Accepted: February 25, 2024

 

Keywords: Lung cancer; Multimodality; Feature fusion; Deep learning; CT images; Computer-aided diagnosis