Optimizing Lung Cancer Classification with Extreme Learning Machine and Ant Lion Optimization for Enhanced Early Detection
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".