Please use this identifier to cite or link to this item:
Title: Multi-view CNN with MLP for diagnosing tuberculosis patients using CT scans and clinically relevant metadata
Authors: Mossa, A.A.
Yibre, A.M.
Çevik, U.
Keywords: Automatic CT Report
Convolutional Neural Network
Deep Learning
Medical Imaging Analysis
Multi-Layer Perceptron
Severity Score
Tuberculosis Detection
Issue Date: 2019
Publisher: CEUR-WS
Abstract: We propose a hybrid approach of multi-view convolutional neural networks with Multi-Layer Perceptron to generate an automatic medical CT report and evaluation of the severity stage of Tuberculosis patients, trained and evaluated on 335 chest 3D CT images and available metadata provided by Im-ageCLEF2019 organizers for the participants of tuberculosis computation track. Transfer learning and data augmentation techniques were applied to avoid over fitting and enhance performance of the model. Our multi-view CNN approach comprises the decomposition of the 3D CT image into 2D axial, coronal and sagittal slices and converting them to PNG format as preliminary to training. At the first stage, coronal and sagittal slices were used to train the CNN classifier using pre-trained AlexNet. In the second stage, MLPs were trained using features extracted during stage one alongside with the provided metadata. Our results ranked 6th and 4th ,with an AUC of 0.763 in predicting whether the severity stage is High or Low, and mean AUC of 0.707 in detecting whether left and right lungs are affected or not, detecting the absence or presence of calcifications, caverns, pleurisy and lung capacity decrease, respectively. © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Description: 20th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2019 -- 9 September 2019 through 12 September 2019 -- -- 149771
ISSN: 1613-0073
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

Files in This Item:
File SizeFormat 
  Until 2030-01-01
464.91 kBAdobe PDFView/Open    Request a copy
Show full item record

CORE Recommender

Page view(s)

checked on Feb 26, 2024

Google ScholarTM


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.