Abdul Jabbar 
, M. Irfan Khtaak, Arif ULLAH
Various forms of cancer have been recognized, all sharing a common objective: the rapid destruction of healthy tissue. To enhance a patients chances of surviving cancer, it is crucial to accurately diagnose and prognose the specific type of underlying disease. Early identification and personalized treatment can potentially improve survival rates. Additionally, it is important to differentiate cancer patients based on their risk levels for disease progression. In the past, data mining and machine learning algorithms have been employed for cancer diagnosis. However, these approaches rely on manually conducted feature extraction techniques, resulting in unreliable categorization. Consequently, precise cancer identification becomes a time-consuming task fraught with the possibility of pathologist errors. In contrast, deep learning has recently gained significant traction in categorization and detection fields, owing to its powerful feature extraction capabilities. Therefore, I utilized a hybrid deep learning model to achieve better accuracy in identifying cases of lung cancer.
Abdul Jabbar M. Irfan Khtaak Arif ULLAH “Using Computer Tomographic (CT) Images A Rebust Hybrid Computer-Aided Deep Learning Framework for Lung Cancer Classification” Internati Vol. 10 Issue 06 PP. 55-63 June 2023. https://doi.org/10.34259/ijew.23.10065563.
