A Novel Machine-Learning-Based Hybrid CNN Model for Tumor Identification in Medical Image Processing

Gaurav Dhiman, Sapna Juneja, Wattana Viriyasitavat, Hamidreza Mohafez, Maryam Hadizadeh, Mohammad Aminul Islam, Ibrahim El Bayoumy and Kamal Gulati
Additional contact information
Gaurav Dhiman: Department of Computer Science, Government Bikram College of Commerce, Patiala 201206, India
Sapna Juneja: KIET Group of Institutions, Delhi NCR, Ghaziabad 110093, India
Wattana Viriyasitavat: Department of Statistics, Chulalongkorn University, Bangkok 10100, Thailand
Hamidreza Mohafez: Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Maryam Hadizadeh: Centre for Sport and Exercise Sciences, Universiti Malaya, Kuala Lumpur 50603, Malaysia
Mohammad Aminul Islam: Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Jalan Universiti, Kuala Lumpur 50603, Malaysia
Ibrahim El Bayoumy: Department of Public Health and Community Medicine, Faculty of Medicine, Tanta City 31527, Egypt
Kamal Gulati: Department of Information Technology, Amity University, Noida 110096, India

Sustainability, 2022, vol. 14, issue 3, 1-13

Abstract: The popularization of electronic clinical medical records makes it possible to use automated methods to extract high-value information from medical records quickly. As essential medical information, oncology medical events are composed of attributes that describe malignant tumors. In recent years, oncology medicine event extraction has become a research hotspot in academia. Many academic conferences publish it as an evaluation task and provide a series of high-quality annotation data. This article aims at the characteristics of discrete attributes of tumor-related medical events and proposes a medical event. The standard extraction method realizes the combined extraction of the primary tumor site and primary tumor size characteristics, as well as the extraction of tumor metastasis sites. In addition, given the problems of the small number and types of annotation texts for tumor-related medical events, a key-based approach is proposed. A pseudo-data-generation algorithm that randomly replaces information in the whole domain improves the transfer learning ability of the standard extraction method for different types of tumor-related medical event extractions. The proposed method won third place in the clinical medical event extraction and evaluation task of the CCKS2020 electronic medical record. A large number of experiments on the CCKS2020 dataset verify the effectiveness of the proposed method.

Keywords: electronic medical records; medical event extraction; migration learning; joint extraction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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