• V N Nevedha
• Poojari Lohith
• B Reddysai
• V Sreeharikrishna
• Shaik Asma

Innovative methods for early detection of lung cancer are urgently needed, since this disease continues to pose a serious threat to world health. This project presents a novel method to increase the precision and efficacy of lung cancer detection through the use of machine learning algorithms. In contrast to conventional approaches that frequently depend on invasive treatments or expensive imaging technologies, this novel approach leverages machine learning capabilities to analyze medical images, such as CT scans, with previously unheard-of precision. This study employs sophisticated deep learning techniques, specifically convolutional neural networks (CNNs), to detect detailed patterns and properties from lung scans. These CNN models gain remarkable accuracy in differentiating between malignant and benign anomalies by training on large datasets of tagged lung images. Furthermore, the models' ability to adapt to a variety of datasets through the use of transfer learning approaches increases their robustness and usefulness in real-world clinical circumstances. In addition, the study investigates ways to improve lung cancer detection models' performance using ensemble learning. This method seeks to resolve the biases and uncertainties included in medical imaging data by merging predictions from several classifiers, such as CNNs, support vector machines and decision trees. This suggested method has demonstrated encouraging results via thorough testing and verification on separate datasets, providing opportunities for more practical and readily available screening tools for the early diagnosis of lung cancer. This will eventually improve the outcomes of patients and survival rates.Innovative methods for early detection of lung cancer are urgently needed, since this disease continues to pose a serious threat to world health. This project presents a novel method to increase the precision and efficacy of lung cancer detection through the use of machine learning algorithms. In contrast to conventional approaches that frequently depend on invasive treatments or expensive imaging technologies, this novel approach leverages machine learning capabilities to analyze medical images, such as CT scans, with previously unheard-of precision. This study employs sophisticated deep learning techniques, specifically convolutional neural networks (CNNs), to detect detailed patterns and properties from lung scans. These CNN models gain remarkable accuracy in differentiating between malignant and benign anomalies by training on large datasets of tagged lung images. Furthermore, the models' ability to adapt to a variety of datasets through the use of transfer learning approaches increases their robustness and usefulness in real-world clinical circumstances. In addition, the study investigates ways to improve lung cancer detection models' performance using ensemble learning. This method seeks to resolve the biases and uncertainties included in medical imaging data by merging predictions from several classifiers, such as CNNs, support vector machines and decision trees. This suggested method has demonstrated encouraging results via thorough testing and verification on separate datasets, providing opportunities for more practical and readily available screening tools for the early diagnosis of lung cancer. This will eventually improve the outcomes of patients and survival rates.

Multimodal Fusion, Deep Learning, Lung Cancer Detection, Early Diagnosis, Convolutional Neural Networks (CNNs)

"A Novel Deep Learning Approach for Early and Accurate Lung Cancer Detection Leveraging Multimodal Data Fusion ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.p118-p122, May-2024, Available :http://www.jetir.org/papers/JETIR2405G19.pdf

"A Novel Deep Learning Approach for Early and Accurate Lung Cancer Detection Leveraging Multimodal Data Fusion ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppp118-p122, May-2024, Available at : http://www.jetir.org/papers/JETIR2405G19.pdf