Tuberculosis Diagnostics and Localization in Chest X-Rays via Deep Learning Models

For decades, tuberculosis (TB), a potentially serious infectious lung disease, continues to be a leading cause of worldwide death. Proven to be conveniently efficient and cost-effective, chest X-ray (CXR) has become the preliminary medical imaging tool for detecting TB. Arguably, the quality of TB diagnosis will improve vastly with automated CXRs for TB detection and the localization of suspected areas, which may manifest TB. The current line of research aims to develop an efficient computer-aided detection system that will support doctors (and radiologists) to become well-informed when making TB diagnosis from patients' CXRs. Here, an integrated process to improve TB diagnostics via convolutional neural networks (CNNs) and localization in CXRs via deep-learning models is proposed. Three key steps in the TB diagnostics process include (a) modifying CNN model structures, (b) model fine-tuning via artificial bee colony algorithm, and (c) the implementation of linear average–based ensemble method. Comparisons of the overall performance are made across all three steps among the experimented deep CNN models on two publicly available CXR datasets, namely, the Shenzhen Hospital CXR dataset and the National Institutes of Health CXR dataset. Validated performance includes detecting CXR abnormalities and differentiating among seven TB-related manifestations (consolidation, effusion, fibrosis, infiltration, mass, nodule, and pleural thickening). Importantly, class activation mapping is employed to inform a visual interpretation of the diagnostic result by localizing the detected lung abnormality manifestation on CXR. Compared to the state-of-the-art, the resulting approach showcases an outstanding performance both in the lung abnormality detection and the specific TB-related manifestation diagnosis vis-à-vis the localization in CXRs.

[1]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[2]  Andrew Y. Ng,et al.  CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning , 2017, ArXiv.

[3]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[4]  Clement J. McDonald,et al.  Automatic Tuberculosis Screening Using Chest Radiographs , 2014, IEEE Transactions on Medical Imaging.

[5]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[6]  D. Adler,et al.  Consolidation in primary pulmonary tuberculosis. , 1953, Thorax.

[7]  Nicolas Le Roux,et al.  Ask the locals: Multi-way local pooling for image recognition , 2011, 2011 International Conference on Computer Vision.

[8]  Ming-Huwi Horng.  Fine-Tuning Parameters of Deep Belief Networks Using Artificial Bee Colony Algorithm , 2017 .

[9]  Marios Anthimopoulos,et al.  Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network , 2016, IEEE Transactions on Medical Imaging.

[10]  Khulood AlYahya,et al.  Artificial Bee Colony Training of Neural Networks , 2013, NICSO.

[11]  J. Goo,et al.  Pulmonary tuberculosis in patients with idiopathic pulmonary fibrosis. , 2004, European journal of radiology.

[12]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[13]  Won-Sook Lee,et al.  Encoder-Decoder CNN Models for Automatic Tracking of Tongue Contours in Real-time Ultrasound Data. , 2020, Methods.

[14]  Ronald M. Summers,et al.  ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases , 2019, Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics.

[15]  K. Buerki,et al.  Pulmonary tuberculosis presenting as mass lesions and simulating neoplasms in adults. , 1998, Australasian radiology.

[16]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.

[17]  Clement J. McDonald,et al.  Lung Segmentation in Chest Radiographs Using Anatomical Atlases With Nonrigid Registration , 2014, IEEE Transactions on Medical Imaging.

[18]  Sameer K. Antani,et al.  Modality-Specific Deep Learning Model Ensembles Toward Improving TB Detection in Chest Radiographs , 2020, IEEE Access.

[19]  Chi-Man Pun,et al.  Training Feed-Forward Artificial Neural Networks with a modified artificial bee colony algorithm , 2020, Neurocomputing.

[20]  Jiwei Liu,et al.  A Locating Model for Pulmonary Tuberculosis Diagnosis in Radiographs , 2019, ArXiv.

[21]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[22]  P. Lakhani,et al.  Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. , 2017, Radiology.

[23]  F. Pfeiffer,et al.  Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization , 2019, Scientific Reports.

[24]  Mustafa Alçi,et al.  A Novel Cloning Template Designing Method by Using an Artificial Bee Colony Algorithm for Edge Detection of CNN Based Imaging Sensors , 2011, Sensors.

[25]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[26]  A. Diacon,et al.  Tuberculous pleural effusions: advances and controversies. , 2015, Journal of thoracic disease.

[27]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[29]  Sameer Antani,et al.  Localizing tuberculosis in chest radiographs with deep learning , 2018, Medical Imaging.

[30]  Yanxia Sun,et al.  Pulmonary Tuberculosis Detection Using Deep Learning Convolutional Neural Networks , 2019, ICVIP.

[31]  Ronald M. Summers,et al.  Learning to Read Chest X-Rays: Recurrent Neural Cascade Model for Automated Image Annotation , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  S. Verma,et al.  Bilateral Nodular Pulmonary Tuberculosis Simulating Metastatic Lung Cancer , 2006 .

[33]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[34]  Hyo-Eun Kim,et al.  A novel approach for tuberculosis screening based on deep convolutional neural networks , 2016, SPIE Medical Imaging.

[35]  Mirko Zimic,et al.  Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images , 2019, PloS one.

[36]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[37]  Jeon-Hor Chen,et al.  Robust Texture Analysis Using Multi-Resolution Gray-Scale Invariant Features for Breast Sonographic Tumor Diagnosis , 2013, IEEE Transactions on Medical Imaging.

[38]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[39]  Kagan Tumer,et al.  Classifier ensembles: Select real-world applications , 2008, Inf. Fusion.

[40]  Mert R. Sabuncu,et al.  Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.

[41]  Natalio Krasnogor,et al.  Nature-inspired cooperative strategies for optimization , 2009 .

[42]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Nurshazlyn Mohd Aszemi,et al.  Hyperparameter Optimization in Convolutional Neural Network using Genetic Algorithms , 2019, International Journal of Advanced Computer Science and Applications.

[44]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[45]  R. Besar,et al.  Biomedical Imaging and Intervention Journal Identification of Masses in Digital Mammogram Using Gray Level Co-occurrence Matrices , 2022 .

[46]  João Francisco Valiati,et al.  Pre-trained convolutional neural networks as feature extractors for tuberculosis detection , 2017, Comput. Biol. Medicine.

[47]  Chang Liu,et al.  TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[48]  T. Serizawa,et al.  Optimization of Convolutional Neural Network Using the Linearly Decreasing Weight Particle Swarm Optimization , 2020, ArXiv.