A Residual-Learning-Based Multi-Scale Parallel-Convolutions- Assisted Efficient CAD System for Liver Tumor Detection

Muazzam Maqsood, Maryam Bukhari, Zeeshan Ali, Saira Gillani, Irfan Mehmood, Seungmin Rho and Young-Ae Jung
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Muazzam Maqsood: Department of Computer Science, COMSATS University Islamabad-Attock Campus, Attock 43600, Pakistan
Maryam Bukhari: Department of Computer Science, COMSATS University Islamabad-Attock Campus, Attock 43600, Pakistan
Zeeshan Ali: R & D Department, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan
Saira Gillani: Department of Computer Science, Bahria University-Lahore, Lahore 54600, Pakistan
Irfan Mehmood: Centre for Visual Computing, University of Bradford, Bradford BD71DP, UK
Seungmin Rho: Department of Industrial Security, Chung-Ang University, Seoul 06974, Korea
Young-Ae Jung: Division of Information Technology Education, Sunmoon University, Asan 31460, Korea

Mathematics, 2021, vol. 9, issue 10, 1-15

Abstract: Smart multimedia-based medical analytics and decision-making systems are of prime importance in the healthcare sector. Liver cancer is commonly stated to be the sixth most widely diagnosed cancer and requires an early diagnosis to help with treatment planning. Liver tumors have similar intensity levels and contrast as compared to neighboring tissues. Similarly, irregular tumor shapes are another major issue that depends on the cancer stage and tumor type. Generally, liver tumor segmentation comprises two steps: the first one involves liver identification, and the second stage involves tumor segmentation. This research work performed tumor segmentation directly from a CT scan, which tends to be more difficult and important. We propose an efficient algorithm that employs multi-scale parallel convolution blocks (MPCs) and Res blocks based on residual learning. The fundamental idea of utilizing multi-scale parallel convolutions of varying filter sizes in MPCs is to extract multi-scale features for different tumor sizes. Moreover, the utilization of residual connections and residual blocks helps to extract rich features with a reduced number of parameters. Moreover, the proposed work requires no post-processing techniques to refine the segmentation. The proposed work was evaluated using the 3DIRCADb dataset and achieved a Dice score of 77.15% and 93% accuracy.

Keywords: liver tumor segmentation; smart healthcare system; residual learning; multi-scale features (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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