General Information
    • ISSN: 1793-8201 (Print), 2972-4511 (Online)
    • Abbreviated Title: Int. J. Comput. Theory Eng.
    • Frequency: Quarterly
    • DOI: 10.7763/IJCTE
    • Editor-in-Chief: Prof. Mehmet Sahinoglu
    • Associate Editor-in-Chief: Assoc. Prof. Alberto Arteta, Assoc. Prof. Engin Maşazade
    • Managing Editor: Ms. Mia Hu
    • Abstracting/Indexing: Scopus (Since 2022), INSPEC (IET), CNKI,  Google Scholar, EBSCO, etc.
    • Average Days from Submission to Acceptance: 192 days
    • APC: 800 USD
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Prof. Mehmet Sahinoglu
Computer Science Department, Troy University, USA
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.

IJCTE 2023 Vol.15(2): 82-89
DOI: 10.7763/IJCTE.2023.V15.1335

A Novel Hybrid U-Net with Custom Triplet Flatten Loss Function for Liver Lesion Detection

Suraj Patil* and Dnyaneshwar K. Kirange

Manuscript received October 22, 2022; revised November 25, 2022; accepted January 19, 2023.

Abstract—Liver cancer ranks sixth among all cancers diagnosed globally. Due to the heterogeneous shape and size of the liver, the manual segmentation of the liver and lesions is a challenging task and time-consuming process. Most of the previous studies in this regard use traditional techniques of image processing to segment the liver and then use handcrafted features to detect lesions and tumors in the liver. The entire process is semi-automatic and results in a loss of information that affects the performance of prediction. Also, deep learning methods employed for liver lesion detection suffer from the misclassification of lesions due to an imbalance of pixel intensities and high processing computational costs. As a result, a new variant U-Net model is designed with a combination of ResNet-18 and ResNet-34 that automatically utilizes 3D contextual information of tumor tissue and detects lesions in the liver. In addition to these, a custom flattened triplet cross entropy function is designed that overcomes the problem of misclassification of lesions due to class imbalance. The novel methodology was evaluated using the benchmark LiTS17 dataset, and the best results were achieved with an accuracy, sensitivity, and specificity of 99.95%, 99.70%, and 99.85%, respectively. We were able to get a considerable reduction in error rate as well as excellent accuracy. The biomedical sector will be transformed as a result of this research.

Index Terms—CT-scan images, deep learning, liver tumor segmentation, flatten triplet cross-entropy loss

Suraj Patil is with Computer Science Department at MPSTME, NMIMS University, Shirpur, India and with SSBT’s COE, KBC North Maharashtra University, Jalgaon, India. Dnyaneshwar K. Kirange is with Computer Engineering Department at SSBT’s College of Engineering and Technology, Bambhori, Jalgaon, India.
*Correspondence: (S.P.)


Cite:Suraj Patil and Dnyaneshwar K. Kirange , "A Novel Hybrid U-Net with Custom Triplet Flatten Loss Function for Liver Lesion Detection," International Journal of Computer Theory and Engineering vol. 15, no. 2, pp. 82-89, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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