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• W4280494910 abstract "Cancer is the uncontrolled growth of abnormal cells that do not function as normal cells. Lung cancer is the leading cause of cancer death in the world, so early detection of lung disease will have a major impact on the likelihood of a definitive cure. Computed Tomography (CT) has been identified as one of the best imaging techniques. Various tools available for medical image processing include data collection in the form of images and algorithms for image analysis and system testing. This study proposes a new diagnosis system for lung cancer based on image processing and artificial intelligence from CT-scan images. In the present study, after noise reduction based on wiener Filtering, Alexnet has been utilized for diagnosing healthy and cancerous cases. The system also uses optimum terms of different features, including Gabor wavelet transform, GLCM, and GLRM to be used in replacing with the network feature extraction part. The study also uses a new modified version of the Satin Bowerbird Optimization Algorithm for optimal designing of the Alexnet architecture and optimal selection of the features. Simulation results of the proposed method on the RIDER Lung CT collection database and the comparison results with some other state-of-the-art methods show that the proposed method provides a satisfying tool for lung cancer diagnosis. The comparison results show that the proposed method with 95.96% accuracy shows the highest value toward the others. The results also show that a higher harmonic mean value for the proposed method with higher F1-score of the method toward the others. Plus, the highest test recall results (98.06%) of the proposed method indicate its higher rate of relevant instances that are retrieved for the images. Therefore, using the proposed method can provide an efficient tool for optimal diagnosis of the Lung Cancer from the CT Images. this shows that using the proposed method as a new deep-learning-based methodology, can provide higher accuracy and can resolve the big problem of optimal hyperparameters selection of the deep-learning-based methodology techniques for the aimed case." @default.
• W4280494910 created "2022-05-22" @default.
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• W4280494910 date "2022-08-01" @default.
• W4280494910 modified "2023-10-18" @default.
• W4280494910 title "Lung cancer diagnosis in CT images based on Alexnet optimized by modified Bowerbird optimization algorithm" @default.
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• W4280494910 doi "https://doi.org/10.1016/j.bspc.2022.103791" @default.
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