Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis

Hanan Abdullah Mengash; Mohammad Alamgeer; Mashael Maashi; Mahmoud Othman; Manar Ahmed Hamza; Sara Saadeldeen Ibrahim; Abu Sarwar Zamani; Ishfaq Yaseen

doi:10.3390/cancers15051591

Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis

Cancers (Basel). 2023 Mar 3;15(5):1591. doi: 10.3390/cancers15051591.

Authors

Hanan Abdullah Mengash¹, Mohammad Alamgeer², Mashael Maashi³, Mahmoud Othman⁴, Manar Ahmed Hamza⁵, Sara Saadeldeen Ibrahim⁵, Abu Sarwar Zamani⁵, Ishfaq Yaseen⁵

Affiliations

¹ Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
² Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, P.O. Box 960, Muhayil 63311, Saudi Arabia.
³ Department of Software Engineering, College of Computer and Information Science, King Saud University, P.O. Box 103786, Riyadh 11543, Saudi Arabia.
⁴ Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt.
⁵ Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, P.O. Box 173, Al-Kharj 16242, Saudi Arabia.

Abstract

Cancer is a deadly disease caused by various biochemical abnormalities and genetic diseases. Colon and lung cancer have developed as two major causes of disability and death in human beings. The histopathological detection of these malignancies is a vital element in determining the optimal solution. Timely and initial diagnosis of the sickness on either front diminishes the possibility of death. Deep learning (DL) and machine learning (ML) methods are used to hasten such cancer recognition, allowing the research community to examine more patients in a much shorter period and at a less cost. This study introduces a marine predator's algorithm with deep learning as a lung and colon cancer classification (MPADL-LC3) technique. The presented MPADL-LC3 technique aims to properly discriminate different types of lung and colon cancer on histopathological images. To accomplish this, the MPADL-LC3 technique employs CLAHE-based contrast enhancement as a pre-processing step. In addition, the MPADL-LC3 technique applies MobileNet to derive feature vector generation. Meanwhile, the MPADL-LC3 technique employs MPA as a hyperparameter optimizer. Furthermore, deep belief networks (DBN) can be applied for lung and color classification. The simulation values of the MPADL-LC3 technique were examined on benchmark datasets. The comparison study highlighted the enhanced outcomes of the MPADL-LC3 system in terms of different measures.

Keywords: MobileNet; colon cancer; computer-aided diagnosis; lung cancer; marine predator’s algorithm.

Abstract

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