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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mallick, Shrabani
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Biomedical image classification using seagull optimization with deep learning for colon and lung cancer diagnosis Manoharan, Thiyagarajan; Velvizhi, Ramalingam; Juluru, Tarun Kumar; Kamal, Shoaib; Mallick, Shrabani; Puliyanjalil, Ezudheen
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1670-1679

Abstract

Traditional health care relies on biomedical image categorization to identify and treat various medical conditions. In machine learning and medical imaging, biomedical image classification for colon and lung cancer diagnosis is significant. The work focuses on building novel models and algorithms to accurately detect and categorize tumorous lesions using computer tomography (CT) scans and histopathology slides. These systems use image processing, deep learning (DL), and convolutional neural networks (CNN) to assist medical professionals diagnose cancer sooner and improve patient outcomes. Biomedical image classification using seagull optimization with deep learning (BIC-SGODL) addresses colon and lung cancer diagnosis. The BIC-SGODL method improves cancer diagnosis using hyperparameter optimized DL model. BIC-SGODL utilizes DenseNet to learn complicated features. The convolutional long short-term memory (CLSTM) standard captures spatiotemporal information in sequential picture data. Finally, the SGO method adjusts hyperparameters to improve model performance and generalization. BIC-SGODL performs well with biomedical image dataset simulations. Thus, medical picture cancer diagnosis may be automated using BIC-SGODL.
Co-Authors Juluru, Tarun Kumar Kamal, Shoaib Manoharan, Thiyagarajan Puliyanjalil, Ezudheen Velvizhi, Ramalingam