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CNN-BO-LSTM: an ensemble framework for prognosis of liver cancer

International Journal of Information Technology (Singapore) 2025年第2期17卷 1103-1109页

作者： K N, Sunil Kumar Kashyap, Pavan P. Bhyratae, Darshan A. Bhyratae, Suhas A. Kalaivani, A. Department of ISE Sri Venkateshwara College of Engineering Bangalore India Department of Electronics & amp Communication Engineering Sri Venkateshwara College of Engineering Bangalore India Computer Science and Engineering Sahyadri College of Engineering and Management Mangaluru India Department of MCA New Horizon College of Engineering Marathahalli Bangalore India Department of CSE-Cyber Security Sri Venkateshwara College of Engineering Bangalore India

Computer-assisted diagnosis (CAD) is preferred for cancer identification across the globe, which relies on computerized image processing. The creation of previous CAD instruments involved a semi-automated approach that employed traditional deep learning techniques. Such techniques are not well versed with CAD instruments in terms of accuracy. Therefore, the given manuscript presents a convolutional neural network normalized architecture embedded with bayesian optimization and long short-term memory (CNN-BO-LSTM) for the identification of liver cancer. Early pre-processing is done on the input magnetic resonance imaging (MRI) scan images to improve clarity. Next, we used dynamic binary classification to apply the accurate and region of interest (ROI) extracting approach. It is followed by automatic retrieval of CNN-based appearances from the ROI approach. For classification purposes, LSTM is used, which categorizes the images as benign or malignant. The proposed design’s testing outcomes, which combine characteristics with CNN-based ROI extraction and LSTM classification, surpassed the current state-of-the-art techniques. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Computer aided diagnosis (CAD) Convolutional neural network (CNN) features extraction and bayesian optimization Long short term memory (LSTM)

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