Published Paper Details:

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, necessitating the development of advanced diagnostic tools to improve early detection and treatment outcomes. This study introduces "DeepLungCareNet", a novel deep learning-based framework specifically designed for the prediction and classification of lung cancer from medical imaging data. Leveraging the power of convolutional neural networks (CNNs) and advanced image processing techniques, "DeepLungCareNet" aims to enhance the accuracy and reliability of lung cancer diagnosis. In this research, we utilized the IQ-OTH/NCCD Lung Cancer Dataset available from Kaggle, comprising thousands of labeled medical images, including computed tomography (CT) scans and X-rays. The dataset was preprocessed through a series of steps, including normalization, augmentation, and segmentation, to ensure optimal input quality for the neural network. Our model architecture was meticulously designed to capture intricate patterns and anomalies within the imaging data, utilizing multiple convolutional layers, pooling layers, and fully connected layers to extract and learn meaningful features. The performance of "DeepLungCareNet" was evaluated using various metrics, such as accuracy, precision, recall, F1-score, and support. Comparative analysis with existing state-of-the-art models demonstrated that "DeepLungCareNet" outperformed traditional methods, achieving superior results in both sensitivity and specificity. Additionally, we employed explainability techniques, such as Grad-CAM, to visualize and interpret the regions of interest that significantly contributed to the model's predictions, thus providing valuable insights for medical practitioners. Our findings underscore the potential of "DeepLungCareNet" as a robust tool for early lung cancer detection, offering promising implications for clinical practice and patient care. The proposed framework not only enhances diagnostic accuracy but also aids in reducing false positives and false negatives, ultimately contributing to better prognosis and treatment planning. Future work will focus on expanding the dataset, incorporating multi-modal data, and exploring transfer learning to further refine and validate the model's capabilities.