• Sanjukta Chakraborty
• Prof. (Dr.) Dilip Kumar Banerjee

Lung cancer remains one of the most prevalent and deadliest forms of cancer worldwide. Early detection and accurate classification are critical for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) techniques, particularly ensemble learning and transfer learning, have shown promising results in the field of medical image analysis, including lung cancer detection and classification. This paper presents a comprehensive study on the application of ensemble learning and transfer learning in the context of lung cancer detection and classification using AI. Ensemble learning techniques combine multiple models to improve predictive performance, while transfer learning leverages knowledge learned from one task to enhance performance on another related task. The proposed approach first involves the acquisition of medical imaging data, such as chest X-rays or computed tomography (CT) scans, from patients diagnosed with lung cancer. Preprocessing techniques are then applied to enhance the quality of the images and prepare them for analysis. Next, an ensemble learning framework is employed, which consists of multiple base classifiers, such as decision trees, support vector machines (SVM), and random forests. These classifiers are trained on subsets of the data using techniques such as bagging, boosting, or stacking to collectively make predictions that are more robust and accurate than those of any individual classifier. In addition to ensemble learning, transfer learning is applied to further improve the performance of the model. Transfer learning involves transferring knowledge from a pre-trained model, often trained on a large dataset, to a new task with a smaller dataset. In the context of lung cancer detection and classification, a pre-trained neural network, such as a convolutional neural network (CNN), is fine-tuned using the acquired medical imaging data to learn relevant features specific to lung cancer. The effectiveness of the proposed approach is evaluated using performance metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Experimental results demonstrate the superiority of the ensemble learning and transfer learning approach compared to individual classifiers or models trained from scratch. Furthermore, the proposed approach exhibits robustness across different datasets and shows promising results in real-world scenarios. The model's ability to accurately detect and classify lung cancer from medical images can potentially assist radiologists in clinical decision-making, leading to earlier detection, more precise diagnosis, and better patient outcomes. This paper highlights the significance of ensemble learning and transfer learning techniques in improving the accuracy and reliability of AI-based lung cancer detection and classification systems. By harnessing the power of these advanced AI methods, we can make significant strides towards combating lung cancer and ultimately saving lives.

Lung Cancer, Artificial Intelligence, Machine Learning, Ensemble Learning, Transfer Learning

"A Study on the Detection and Classification of Lung Cancer using Ensemble Learning and Transfer Learning of Artificial Intelligence ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.f794-f821, May-2024, Available :http://www.jetir.org/papers/JETIR2405589.pdf

"A Study on the Detection and Classification of Lung Cancer using Ensemble Learning and Transfer Learning of Artificial Intelligence ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppf794-f821, May-2024, Available at : http://www.jetir.org/papers/JETIR2405589.pdf