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Ebrahim Ghajari, Negin, Fathi, Abdolhossein. (1402). Detection and Classification of Lung Cancer in Histopathology Images Using Deep Learning. سامانه مدیریت نشریات علمی دانشگاه اصفهان, 11(1), 19-28. doi: 10.22108/jcs.2024.140356.1139
Negin Ebrahim Ghajari; Abdolhossein Fathi. "Detection and Classification of Lung Cancer in Histopathology Images Using Deep Learning". سامانه مدیریت نشریات علمی دانشگاه اصفهان, 11, 1, 1402, 19-28. doi: 10.22108/jcs.2024.140356.1139
Ebrahim Ghajari, Negin, Fathi, Abdolhossein. (1402). 'Detection and Classification of Lung Cancer in Histopathology Images Using Deep Learning', سامانه مدیریت نشریات علمی دانشگاه اصفهان, 11(1), pp. 19-28. doi: 10.22108/jcs.2024.140356.1139
Ebrahim Ghajari, Negin, Fathi, Abdolhossein. Detection and Classification of Lung Cancer in Histopathology Images Using Deep Learning. سامانه مدیریت نشریات علمی دانشگاه اصفهان, 1402; 11(1): 19-28. doi: 10.22108/jcs.2024.140356.1139

Detection and Classification of Lung Cancer in Histopathology Images Using Deep Learning

Journal of Computing and Security
مقاله 3، دوره 11، شماره 1، فروردین 2024، صفحه 19-28    اصل مقاله (1.14 M)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22108/jcs.2024.140356.1139
نویسندگان
Negin Ebrahim Ghajari1؛ Abdolhossein Fathi* 2
1Computer Engineering Department, Razi University, Kermanshah, Iran.
2Department of Computer Engineering, Razi University, Kermanshah, Iran.
چکیده
In recent years, artificial intelligence has been used to diagnose and classify cancers using different deep learning-based models. Although they have good overall accuracy, they need high computation resources and execution time, and also have low accuracy, precision, and sensitivity. To this end, we try to employ a new model named the "EfficientNetB0" model with appropriate preprocessing to obtain high precision and sensitivity at a relatively low computation time for diagnosing lung cancer. The EfficientNetB0 model consists of 7 blocks, and each block includes one layer of mobile convolution (MB-Conv) and squeeze-excitation (SE) blocks. EfficientNetB0 has a higher accuracy compared to other common deep learning models due to incorporating a compound coefficient approach. The proposed model is evaluated on the histopathology images dataset and the obtained accuracy of the model is 0.9258. Also, its precision and sensitivity are 0.942 and 0.967, respectively, and these show the superiority of this model compared to existing methods.
کلیدواژه‌ها
Histopathology Images؛ EfficienetNetB0 Model؛ Deep Leaning؛ Lung Cancer Diagnosis and Classification؛ Digital Pathology
سایر فایل های مرتبط با مقاله
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