FPA-based weighted average ensemble of deep learning models for classification of lung cancer using CT scan images.
Authors
Affiliations (15)
Affiliations (15)
- Jiading District Central Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China.
- Department of Information Technology, Bharati Vidyapeeth's College of Engineering, New Delhi, India.
- Department of CSE, Chandigarh College of Engineering and Technology, Chandigarh, India.
- Galgotias Multi-Disciplinary Research & Development Cell (G-MRDC), Galgotias University, Greater Noida, Uttar Pradesh, 201308, India.
- Department of CSE, Chandigarh College of Engineering and Technology, Chandigarh, India. [email protected].
- Information Systems Department, HECI School, Jeddah, Saudi Arabia.
- Hong Kong Metropolitan University, Hong Kong, SAR, China.
- Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India.
- Immersive virtual reality research group, Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia.
- Computer Science Department, HECI School, Dar Alhekma University, Jeddah, Saudi Arabia.
- Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan. [email protected].
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan. [email protected].
- Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, India. [email protected].
- School of Cybersecurity, Korea University, Seoul, South Korea. [email protected].
- Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul, 02447, Korea. [email protected].
Abstract
Cancer is among the most dangerous diseases contributing to rising global mortality rates. Lung cancer, particularly adenocarcinoma, is one of the deadliest forms and severely impacts human life. Early diagnosis and appropriate treatment significantly increase patient survival rates. Computed Tomography (CT) is a preferred imaging modality for detecting lung cancer, as it offers detailed visualization of tumor structure and growth. With the advancement of deep learning, the automated identification of lung cancer from CT images has become increasingly effective. This study proposes a novel lung cancer detection framework using a Flower Pollination Algorithm (FPA)-based weighted ensemble of three high-performing pretrained Convolutional Neural Networks (CNNs): VGG16, ResNet101V2, and InceptionV3. Unlike traditional ensemble approaches that assign static or equal weights, the FPA adaptively optimizes the contribution of each CNN based on validation performance. This dynamic weighting significantly enhances diagnostic accuracy. The proposed FPA-based ensemble achieved an impressive accuracy of 98.2%, precision of 98.4%, recall of 98.6%, and an F1 score of 0.985 on the test dataset. In comparison, the best individual CNN (VGG16) achieved 94.6% accuracy, highlighting the superiority of the ensemble approach. These results confirm the model's effectiveness in accurate and reliable cancer diagnosis. The proposed study demonstrates the potential of deep learning and neural networks to transform cancer diagnosis, helping early detection and improving treatment outcomes.