Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm

• Published in: PloS one Vol. 18; no. 8; p. e0285796
• Main Authors: Mohamed, Tehnan I. A., Oyelade, Olaide N., Ezugwu, Absalom E.
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 17.08.2023
• Subjects: Accuracy; Algorithms; Analysis; Bias; Biology and Life Sciences; Care and treatment; Classification; Computed tomography; Computer and Information Sciences; Configuration management; CT imaging; Datasets; Deep learning; Design; Diagnosis; Ebola virus; Engineering and Technology; Evaluation; Feature selection; Heuristic methods; Lung cancer; Lung diseases; Machine learning; Medical imaging; Medicine and Health Sciences; Methods; Modelling; Neural networks; Optimization; Optimization algorithms; Optimization techniques; Outdoor air quality; Performance assessment; Physical Sciences; Public health; Research and Analysis Methods; Search algorithms; Viruses; South Africa
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0285796

Recently, research has shown an increased spread of non-communicable diseases such as cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in recent years. Early lung cancer diagnosis and detection would reliably promote safety and the survival of many lives globally. The precise classification of lung cancer using medical images will help physicians select suitable therapy to reduce cancer mortality. Much work has been carried out in lung cancer detection using CNN. However, lung cancer prediction still becomes difficult due to the multifaceted designs in the CT scan. Moreover, CNN models have challenges that affect their performance, including choosing the optimal architecture, selecting suitable model parameters, and picking the best values for weights and biases. To address the problem of selecting optimal weight and bias combination required for classification of lung cancer in CT images, this study proposes a hybrid metaheuristic and CNN algorithm. We first designed a CNN architecture and then computed the solution vector of the model. The resulting solution vector was passed to the Ebola optimization search algorithm (EOSA) to select the best combination of weights and bias to train the CNN model to handle the classification problem. After thoroughly training the EOSA-CNN hybrid model, we obtained the optimal configuration, which yielded good performance. Experimentation with the publicly accessible Iraq-Oncology Teaching Hospital / National Center for Cancer Diseases (IQ-OTH/NCCD) lung cancer dataset showed that the EOSA metaheuristic algorithm yielded a classification accuracy of 0.9321. Similarly, the performance comparisons of EOSA-CNN with other methods, namely, GA-CNN, LCBO-CNN, MVO-CNN, SBO-CNN, WOA-CNN, and the classical CNN, were also computed and presented. The result showed that EOSA-CNN achieved a specificity of 0.7941, 0.97951, 0.9328, and sensitivity of 0.9038, 0.13333, and 0.9071 for normal, benign, and malignant cases, respectively. This confirms that the hybrid algorithm provides a good solution for the classification of lung cancer.