Feature extraction-based liver tumor classification using Machine Learning and Deep Learning methods of computed tomography images

Abstract

The liver is an important and multifunctional human organ. Early and accurate diagnosis of a liver tumor can save lives. Computed Tomography (CT) images provide comprehensive information for liver tumor diagnosis using feature extraction techniques. These extracted features help classify liver tumors using Machine Learning (ML) and Deep Learning (DL) methods. For this research, twelve 1hundred CT images were acquired from the Radiology Department of Nishter Medical University & Hospital. The noise was removed using Gabor Filter after converting CT images into grayscale. Image quality was enhanced by adopting Histogram Equalization (HE), and finally, the Image’s edges and boundaries were improved using a smoothening and sharpening algorithm. Preprocessed images were forwarded to extract six features: Histogram, Run-length, Co-occurrence, Autogressive, Gradient, and Wavelet Transform. A major focus of this research is to evaluate that ML methods produced good accuracy using already extracted features while DL Algorithms could not produce better results. Firstly, ML methods such as Decision Tree (DT), Random Forest (RF), Boost, and Support Vector Machine (SVM) are deployed using an already extracted feature list containing all six features. It was observed that DT, RF, Boost, and SVM produced 96.5%, 99.6%, 99.7%, and 98.0% classification accuracy. After that, DL Algorithms such as Neural Networks (NN), Long-Short Term Memory (LSTM), Bi-Directional Long Short Term Memory (Bi-LSTM), and Convolutional Neural Networks (CNN) were deployed. The results showed that NN, LSTM, Bi-LSTM, and CNN produced 50.0%, 53.0%, 54.0%, and 54.0% accuracy respectively. To validate the major focus of this research, finally, Pre-trained DL Algorithms such as Residual Network 50 (Resnet50), Visual Geometry Group 16 (VGG16) and LSTM + CNN were deployed. Results showed that Resnet50, VGG16, and LSTM + CNN attained 78.0%, 88.0%, and 97.0% accuracy respectively. Hence, ML methods performed better using already extracted features, while DL Algorithms could not produce promising results on these extracted features.

KEYWORD:

• CT images
• computer vision
• Deep Learning
• feature extraction
• liver tumor
• Machine Learning

Reviewing Editor:

• Jenhui Chen, Chang Gung University, Taiwan; Xiangxiang Zeng, Xiamen University, China

Subjects:

• Algorithms & Complexity
• Artificial Intelligence
• Computer Science (General)

Acknowledgment

This research is funded by the Institute of Southern Punjab, Multan, Pakistan and University of Vaasa, Finland.

Authors’ contributions

All authors contributed equally to accomplish this study. In addition, all authors read and approved the final manuscript.

Code availability

The code will be available upon request to the corresponding author.

Conflict of interests

The authors have no conflict of interests.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Data availability

The data will be available upon request to the corresponding author.

Ethical approval

Not applicable.

Additional information

Funding

This work was supported by the Zhejiang Province Traditional Chinese Medicine Science and Technology Project under Grant 2024ZF106.

Notes on contributors

Mubasher H. Malik

Mubasher Malik is the Head of the Computer Science Department with the Institute of Southern Punjab. He has been involved in more than 40 publications. His main area of research are Image and Natural Languageprocessing.

Hamid Ghous

Hamid Ghous is the Head of Research for the Computer Science Department with the Institute of Southern Punjab (ISP). He is also leading the Vision, Linguistic, and Machine Learning Laboratory, ISP. He has authored and coauthored more than 30 publications in the past. His main area of research is machine and deep learningmethods.

Tahir Rashid

Tahir Rashid received the B.S. degree in computer science from Mohi ud din Islamic University Islamabad, Pakistan, in 2004, the M.S. degree in Computer Science from Muhammad Ali Jinnah University, Islamabad (M.A.J.U) in 2008. He is currently an Assistant Professor with the Department of Computer Sciences, COMSATS University Islamabad, Vehari Campus, Pakistan. His particular research interests include machine learning, data mining, software maintenance, and developing practical tools to assist softwareengineers.

Bibi Maryum

Bibi Maryum holds MBBS from University if health sciences and currently a research enthusiast. Her research interests includes AI application in gastroenterology, endocrinology and oncology.

Zhang Hao

Zhang Hao, Ph.D., is associate professor in the School of Public Health at Hangzhou Normal University. Her primary research focus on the exploration of health big data and health system simulation modeling.

Qasim Umer

Qasim Umer is working as an Assistant Professor with the Department of Computer Sciences, COMSATS University Islamabad, Vehari Campus, Pakistan. He received BS degree in Computer Science from Punjab University, Pakistan in 2006, MS degree in .Net Distributed System Development from University of Hull, UK in 2009, MS degree in Computer Science from University of Hull, UK in 2013, and Ph.D. degree from Beijing Institute of Technology, China. He is particularly interested in machine/deep learning, NLP, and IoTs. He is also interested in developing practical tools to assist software engineers.