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@article{180829,
        author = {Ulinjala Gayathri and Allam Venkata Sahithi and Jaada Sampath Kumar and Nangunuri Sri Ram and J.Naresh Kumar},
        title = {Hybrid Deep Learning Framework For Lung Cancer Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2731-2736},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180829},
        abstract = {This project pioneers a hybrid deep learning approach to automate and enhance the diagnosis of lung cancer, addressing a critical need for faster and more accurate CT scan analysis. Traditionally, radiologists require significant time to interpret CT scans and distinguish between benign and malignant nodules, with the added risk of human error. This system integrates Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to detect and analyze cancerous regions in lung images with high precision. Specifically, a CNN-ResNet architecture is employed for image segmentation and tumor localization in volumetric CT data, while a Bi-directional Long Short-Term Memory (BiLSTM) network is used to model temporal and morphological feature changes over time. This dual-framework enables the system to learn both spatial and sequential patterns, offering a significant leap in diagnostic accuracy and speed. The proposed method not only reduces workload for medical professionals but also enhances early detection and treatment planning in clinical oncology.},
        keywords = {Deep Learning, Lung Cancer Detection, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Medical Imaging, Feature Extraction, CT scans, Hybrid Framework, Early Diagnosis, Spatial-Temporal Analysis.},
        month = {June},
        }