Optimizing Lung Cancer Classification with Extreme Learning Machine and Ant Lion Optimization for Enhanced Early Detection

Lung cancer emerges as a notable cancer affecting individuals of all genders on a global scale. Timely detection in its early stages significantly increases the chances of survival. In recent years, the advent of automatic lung cancer detection systems has played a significant role in enhancing diagnostic rates. Despite the advantages presented by machine learning models over traditional methods and their breakthroughs in various image classification tasks, accurately classifying lung cancer remains a challenge. This challenge is attributed to the complexity involved in selecting an appropriate machine learning model and fine-tuning hyperparameters. This paper aims to enhance the performance of a lung cancer classification system by optimizing hyperparameters in the Extreme Learning Machine (ELM) using metaheuristic optimization algorithms. To achieve this, Ant Lion Optimization algorithms are employed to determine optimal weight values for ELM. The novelty of this work lies in the application of ALO to enhance the performance of ELM specifically for lung cancer diagnosis, addressing a crucial gap in existing methodologies. Initially, features are extracted from Convolutional Neural Network (CNN). Subsequently, the optimal weight values and features are utilized in the ELM for the classification of Lung CT images as benign or malignant. The impact of applying hyperparameter optimization is assessed on two benchmark datasets, LIDC-IDRI and KAGGLE. The accuracy of lung cancer prediction using our method reaches 99.5% on the LIDC-IDRI dataset and 99.3% on the KAGGLE dataset. The findings of this study suggest that the proposed method outperforms existing approaches in the diagnosis of lung cancer.