Lung Lesion Identification Using Geometrical Feature and Optical Flow Method from Computed Tomography Scan Images

Abdullah, Mohd Firdaus; Sulaiman, Siti Noraini; Osman, Muhammad Khusairi; Karim, Noor Khairiah A.; Setumin, Samsul; Ani, Adi Izhar Che

doi:10.1007/978-3-031-34873-0_7

Intelligent Multimedia Signal Processing for Smart Ecosystems 2023

DOI: 10.1007/978-3-031-34873-0_7

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Lung Lesion Identification Using Geometrical Feature and Optical Flow Method from Computed Tomography Scan Images

Mohd Firdaus Abdullah,

Siti Noraini Sulaiman,

Muhammad Khusairi Osman

et al.

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2024

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Cited by 1 publication

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Lung Cancer Classification Using Deep Learning Hybrid Model

Jain,

Jaidka

2024

Future of AI in Medical Imaging

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Abnormal growths in the lungs caused by disease. The classification of CT scans is accomplished by applying machine learning strategies. Classification methods based on deep learning, such as support vector machines, can categorize a wide variety of image datasets and produce segmentation results of the highest caliber. In this work, we suggested a method for deep feature extraction from images by altering SVM and CNN and then applying the hybrid model resulting from those modifications (NNSVLC). For this investigation, the Kaggle dataset will be utilized. The proposed method was found to be accurate 91.7% of the time, as determined by the results of the experiments.

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Lung Cancer Classification Using Deep Learning Hybrid Model

Jain,

Jaidka

2024

Future of AI in Medical Imaging

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Lung Lesion Identification Using Geometrical Feature and Optical Flow Method from Computed Tomography Scan Images

Cited by 1 publication

References 49 publications

Lung Cancer Classification Using Deep Learning Hybrid Model

Lung Cancer Classification Using Deep Learning Hybrid Model

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