A Deep-Learning Model Based on Transfer-Learning Technique for Detecting and Classifying Anomalies in Lungs Images
Mostafa, B., Keshk, A., Sakr, M. (2023). A Deep-Learning Model Based on Transfer-Learning Technique for Detecting and Classifying Anomalies in Lungs Images. EKB Journal Management System, 10(3), 63-72. doi: 10.21608/ijci.2023.236215.1134
Bassant Mostafa; Arabi Keshk; Mohamed Sakr. "A Deep-Learning Model Based on Transfer-Learning Technique for Detecting and Classifying Anomalies in Lungs Images". EKB Journal Management System, 10, 3, 2023, 63-72. doi: 10.21608/ijci.2023.236215.1134
Mostafa, B., Keshk, A., Sakr, M. (2023). 'A Deep-Learning Model Based on Transfer-Learning Technique for Detecting and Classifying Anomalies in Lungs Images', EKB Journal Management System, 10(3), pp. 63-72. doi: 10.21608/ijci.2023.236215.1134
Mostafa, B., Keshk, A., Sakr, M. A Deep-Learning Model Based on Transfer-Learning Technique for Detecting and Classifying Anomalies in Lungs Images. EKB Journal Management System, 2023; 10(3): 63-72. doi: 10.21608/ijci.2023.236215.1134

A Deep-Learning Model Based on Transfer-Learning Technique for Detecting and Classifying Anomalies in Lungs Images

IJCI. International Journal of Computers and Information
Article 10, Volume 10, Issue 3, November 2023, Page 63-72  XML PDF (700.95 K)
Document Type: Original Article
DOI: 10.21608/ijci.2023.236215.1134
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Authors
Bassant Mostafa email 1; Arabi Keshk2; Mohamed Sakr3
1Computer science department, Higher technological institute, 10th of Ramadan city, Egypt
2Faculty of Computer and Information Menoufia University
3Computer Science Faculty of Computers and Information Menoufia-University Menoufia, Egypt
Abstract
Over the past decade, there has been a marked increase in interest in the automated identification of malignant tumors, largely due to the demand for an early and precise diagnosis that would lead to the best available therapy for the impending risk. As part of this effort, a variety of machine-learning and artificially intelligent approaches have been used to produce reliable aiding tools. To improve the automatic recognition and diagnosis of problematic lung areas, a deep learning model relying on the transfer learning approach is constructed in this research. VGG16, VGG19, and Inception-V3 are employed for the extraction of features from the IQ-OTHNCCD lung cancer dataset. According to experimental findings, transfer-learning models employing the SVM classifier were more effective than those utilizing the softmax function classifier at classifying CT scan images of the used dataset, Results from experiments demonstrate that the VGG16 model is effective for diagnosing lung cancer exceeding other existing models utilizing the same dataset.
Keywords
Keywords— Machine learning; transfer learning; deep learning; lung cancer; biomedical image classification
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