Authors : Jaya Shrivastav , Dr. Ranu Pandey
DOI : 10.46335/IJIES.2024.9.6.21
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Abstract – Lung cancer continues to be one of the most lethal malignancies worldwide, primarily due to its late-stage detection and the complexity of accurate diagnosis using traditional methods. Early diagnosis significantly enhances treatment outcomes, yet current clinical practices, which often rely on the manifestation of symptoms and manual evaluation of imaging, are time-consuming and prone to human error. With the proliferation of advanced imaging modalities such as Computed Tomography (CT), Low-Dose CT (LDCT), Positron Emission Tomography (PET), and Magnetic Resonance Imaging (MRI), automated systems have emerged to assist in the identification and classification of lung cancer. This review provides a comprehensive examination of the latest developments in automated lung cancer detection, with a focus on machine learning and deep learning approaches, including Convolutional Neural Networks (CNNs), 3D CNNs, Capsule Networks, and hybrid models.
