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Detection of Liver Cancer Using Modified Fuzzy Clustering and Decision Tree Classifier in CT Images

  • Mathematical Method in Pattern Recognition
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Abstract

Manual detection and characterization of liver cancer using computed tomography (CT) scan images is a challenging task. In this paper, we have presented an automatic approach that integrates the adaptive thresholding and spatial fuzzy clustering approach for detection of cancer region in CT scan images of liver. The algorithm was tested in a series of 123 real-time images collected from the different subjects at Institute of Medical Science and SUM Hospital, India. Initially the liver was separated from other parts of the body with adaptive thresholding and then the cancer affected lesions from liver was segmented with spatial fuzzy clustering. The informative features were extracted from segmented cancerous region and were classified into two types of liver cancers i.e., hepatocellular carcinoma (HCC) and metastatic carcinoma (MET) using multilayer perceptron (MLP) and C4.5 decision tree classifiers. The performance of the classifiers was evaluated using 10-fold cross validation process in terms of sensitivity, specificity, accuracy and dice similarity coefficient. The method was effectively detected the lesion with accuracy of 89.15% in MLP classifier and of 95.02% in C4.5 classifier. This results proves that the spatial fuzzy c-means (SFCM) based segmentation with C4.5 decision tree classifier is an effective approach for automatic recognition of the liver cancer.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SOA Deemed to be University, Odisha, India

    Amita Das

  2. Department of Pharmacology, SCB Medical College and Hospital, Odisha, India

    Priti Das

  3. Department of Surgical Oncology, IMS & SUM Hospital, SOA Deemed to be University, Odisha, India

    S. S. Panda

  4. School of Electronics Engineering, KIIT Deemed to be University, Odisha, India

    Sukanta Sabut

Authors
  1. Amita Das
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  2. Priti Das
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  4. Sukanta Sabut
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Correspondence to Sukanta Sabut.

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Amita Das is a research scholar in the Department of Electronics and Communication Engineering, Institute of Technical Education and Research, Siksha SOA Deemed to be University Anusandhan University, Odisha, India. She received his B. Tech Degree in Electronics and Communication Engineering from BPUT, Odisha, in 2009 and M. Tech in Communication System Engineering from Siksha SOA Deemed to be University Anusandhan University, Odisha, India in 2011. She has over 4 years of teaching and research experience. She has published research papers in journals and conferences. Her research interest includes signal and image processing.

Priti Das is an Associate Professor in the Department of Pharmacology, SCB Medical College and Hospital, Cuttack, Odisha, India. She is Editor-in-Chief, International Journal of Telemedicine and Clinical Practices, published from Inderscience Publishing House.

Soumya S. Panda received MD in Medicine, DM in Oncology. Presently working as Associate Professor, Department of Medical Oncology IMS & SUM Hospital Bhubaneswar, Odisha. His research concerns diagnosis, monitoring, and screening for cancer.

S. K. Sabut is working as Associate Professor, School of Electronics Engineering, KIIT University, India. He received his Diploma in Rehabilitation Engineering, BE in Electronics and Communication, M. Tech in Biomedical Instrumentation and PhD in Medical Science and Technology from IIT, Kharagpur in 2010. He has over 18 years of experience in both teaching and research. His research interest includes biomedical signal and image processing, neural engineering and biomedical instrumentation. He has published research papers in international journals and conferences. He is a member of IEEE, IFESS, Rehabilitation council of India, and the Institution of Engineers (India).

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Das, A., Das, P., Panda, S.S. et al. Detection of Liver Cancer Using Modified Fuzzy Clustering and Decision Tree Classifier in CT Images. Pattern Recognit. Image Anal. 29, 201–211 (2019). https://doi.org/10.1134/S1054661819020056

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  • DOI: https://doi.org/10.1134/S1054661819020056

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