Epilepsy Radiology Reports Classification Using Deep Learning Networks


Bayrak S., YÜCEL DEMİREL E., TAKCI H.

CMC-COMPUTERS MATERIALS & CONTINUA, vol.70, no.2, pp.3589-3607, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 70 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.32604/cmc.2022.018742
  • Journal Name: CMC-COMPUTERS MATERIALS & CONTINUA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.3589-3607
  • Keywords: Epilepsy, radiology text report analysis, natural language processing, feature engineering, index-based word encoding, deep learning networks-based text classification, CONVOLUTIONAL NEURAL-NETWORK, MACHINE, MRI, INFORMATION, SYSTEM
  • Istanbul University-Cerrahpasa Affiliated: Yes

Abstract

The automatic and accurate classification of Magnetic Resonance Imaging (MRI) radiology report is essential for the analysis and interpreta-tion epilepsy and non-epilepsy. Since the majority of MRI radiology reports are unstructured, the manual information extraction is time-consuming and requires specific expertise. In this paper, a comprehensive method is proposed to classify epilepsy and non-epilepsy real brain MRI radiology text reports automatically. This method combines the Natural Language Processing tech-nique and statistical Machine Learning methods. 122 real MRI radiology text reports (97 epilepsy, 25 non-epilepsy) are studied by our proposed method which consists of the following steps: (i) for a given text report our systems first cleans HTML/XML tags, tokenize, erase punctuation, normalize text, (ii) then it converts into MRI text reports numeric sequences by using index -based word encoding, (iii) then we applied the deep learning models that are uni-directional long short-term memory (LSTM) network, bidirectional long short-term memory (BiLSTM) network and convolutional neural network (CNN) for the classifying comparison of the data, (iv) finally, we used 70% of used for training, 15% for validation, and 15% for test observations. Unlike previous methods, this study encompasses the following objectives: (a) to extract significant text features from radiologic reports of epilepsy disease; (b) to ensure successful classifying accuracy performance to enhance epilepsy data attributes. Therefore, our study is a comprehensive comparative study with the epilepsy dataset obtained from numeric sequences by using index-based word encoding method applied for the deep learning models. The traditional method is numeric sequences by using index-based word encoding which has been made for the first time in the literature, is successful feature descriptor in the epilepsy data set. The BiLSTM network has shown a promising performance regarding the accuracy rates. We show that the larger sized medical text reports can be analyzed by our proposed method.