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@article{163949,
        author = {Soham Deshmukh and Milind Rane  and Aarya Deshmukh and Akanksha Deshmukh and Om Deshmukh},
        title = {Lung Segmentation using Python},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {2252-2258},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=163949},
        abstract = {— This paper presents a lung segmentation system for computed tomography (CT) scans using Python. The system is built upon a custom dataset class that integrates essential functionalities for loading and preprocessing medical imaging data. The dataset class incorporates methods for accessing CT scans and corresponding lung masks, enabling the preparation of data for segmentation tasks.
Furthermore, the system accommodates various segmentation scenarios by adjusting the masks based on specified classes, enhancing its adaptability to different classification requirements. Notably, it employs advanced techniques, including image resizing and intensity normalization, crucial for model training and performance optimization.
The proposed lung segmentation system is a versatile tool equipped to handle diverse CT imaging datasets, demonstrating potential applicability in aiding medical professionals in precise lung tissue delineation for diagnostic and treatment purposes in pulmonary healthcare.
},
        keywords = {Lung Segmentation, Computed Tomography (CT), Medical Imaging, Convolutional Neural Network (CNN), Deep Learning, Image Processing, Segmentation Masks,2D/3D Segmentation, Data Preprocessing, Medical Image Analysis.},
        month = {},
        }