An Efficient Liver Disease Prediction Using Mask-Regional Convolutional Neural Network and Pelican Optimization Algorithm

Abstract

Various prediction approaches regarding liver diseases have been developed. Still, they are expensive and more complex. This work aims to design an effective method for identifying liver diseases at earlier stages. This paper presents a modified Mask-regionalconvolutional neural network (MRCNN) architecture for non-invasive liver disease prediction. The Pelican Optimization Algorithm (POA) is used to balance the bounding box regression and mask branching training losses of the RCNN model. The liver disease features are extracted from three datasets namely Indian liver patient records, Hepatitis C, and Cirrhosis Prediction datasets. The POA-modified MRCNN model is mainly used to identify the interrelationship that exists between different laboratory measurements and diagnoses. The efficiency of the proposed model is compared with different state-of-art methods such as Opposition-based Laplacian Equilibrium Optimizer, Adaptive Hybridized Deep CNN, SVM, and Tree-based classifiers in terms of Accuracy, Precision, Recall, F-measure, and Mathews Correlation Coefficient (MCC). The proposed model offers an MCC value of 94.89, an accuracy of 98%, a precision of 96.2%, an F-measure of 97.3%, and a recall of 95% respectively. The results demonstrate the efficiency of the proposed model in predicting liver disease at an early stage via automatic screening and minimizing the burden of physicians.

KEYWORDS:

• Cirrhosis
• Convolutional neural network
• Classification
• Liver diseases
• Modified mask-regional cloud-based healthcare

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

J. Aswini

J Aswini She did her BSc in computer science, Master of Computer Application, Master of Computer Science and engineering and PhD in computer science and engineering in 2000, 2003, 2011 and 2020 respectively from Madras University, Anna University and MAHER (Meenakshi Academy of Higher Education and Research), India. At present, she is working with Department of Computer Science and Engineering at Sree Vidyanikethan Engineering College, Tirupati, Andhra Pradesh (India). She has fourteen years of teaching experience and more than a year of industry experience, her research interests include cloud computing, IoT, AI (ML) and data science. Corresponding author. Email: [email protected]

B. Yamini

B Yamini completed her Bachelor of Engineering in computer science and engineering from Mailam Engineering College, in the year 2003. She pursued her Master of Technology in information technology from Sathyabama University, Chennai in the year 2007. She acquired the Doctor of Philosophy in computer science and engineering from Sathyabama Institute of Science and Technology, Chennai in the year 2020. She published papers in various international and national conferences and journals. She is currently working at SRM Institute of Science and Technology, College of Engineering and Technology in the Department of Networking and Communications. Her areas of interest include network security, cyber forensics, image processing, information retrieval system, machine learning, deep learning and cloud computing. Email: [email protected]

K. Venkata Ramana

K Venkata Ramana received PhD degree from Jawaharlal Nehru Technological University Hyderabad, India in 2021, and Master's in Computer Science and Engineering from Jawaharlal Nehru Technological University Hyderabad, India in 2010. He is currently working as an assistant professor in the Department of Computer Science & Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India. Acted as head of the Master of Computer Applications Department for 10 years at Bhoj Reddy Engineering College for Women, Hyderabad, India. He has 20 years of teaching experience and his research areas of interest include data mining for software engineering, machine learning, deep learning, and cloud computing. He has published 15 papers in various renowned international journals and conferences on source code mining. Email: [email protected]

J. Jegan Amarnath

J Jegan Amarnath obtained his Bachelor's degree in computer science and engineering from MS University in 1998. Then he obtained his Master's degree in computer science and engineering from Satyabama University. He is currently in the Department of Computer Science and Engineering at Sri Sairam Engineering College, Chennai, India. He has also obtained CCNA certifications and is a certified Cisco networking academy instructor. His specializations include machine learning, deep learning, data analytics, networking, and virtual reality. His current research interests are data mining and natural language processing. He is a member of the IEEE and a member of the ISTE. He has authored many books. Email: [email protected]