@Article{yolov7,
	author = {Chien-Yao Wang and Alexey Bochkovskiy and Hong-Yuan Mark Liao},
	title = {YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
	journal = {https://arxiv.org/pdf/2207.02696.pdf},
	year = {2022},
	OPTkey = {},
	OPTmonth = {},
	OPTnote = {},
	OPTannote = {},
}

@Article{XCOVNet,
	author = {Vishu Madaan and Aditya Roy and Charu Gupta and Prateek Agrawal and Anand Sharma and Cristian Bologa and Radu Prodan},
	title = {XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks},
	journal = {New Gener. Comput.},
	year = {2021},
	OPTkey = {},
	volume = {39},
	number = {3-4},
	pages = {271-278},
	OPTmonth = {},
	OPTnote = {},
	OPTannote = {},
}

@article{1,
title = {Prediction of Type 2 Diabetes using Machine Learning Classification Methods},
journal = {Procedia Computer Science},
volume = {167},
pages = {706-716},
year = {2020},
note = {International Conference on Computational Intelligence and Data Science},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2020.03.336},
url = {https://www.sciencedirect.com/science/article/pii/S1877050920308024},
author = {Neha Prerna Tigga and Shruti Garg},
keywords = {Logistic Regression, kNN, SVM, Naïve Bayes, DT, Random Forest, AUC, MCC},
abstract = {Over 30 million people in India are suffering from diabetes and many others are under the risk. Thus, early diagnosis and treatment is required to prevent diabetes and its associated health problems. This study aims to assess the risk of diabetes among individuals based on their lifestyle and family background. The risk of Type 2 diabetes was predicted using different machine learning algorithms as these algorithms are highly accurate which is very much required in the health profession. Once the model will be trained with good accuracy, then individuals can self-assess the risk of diabetes. In order to conduct the experiment, 952 instances have been collected through an online and offline questionnaire including 18 questions related to health, lifestyle and family background. The same algorithms were also applied to the Pima Indian Diabetes database. The performance of Random Forest Classifier is found to be most accurate for both datasets.}
}

@Article{2,
  author  = {Clifford B. Kim and Grayson W. Armstrong},
  journal = {International Ophthalmology Clinics},
  title   = {Characterizing Infectious Keratitis Using Artificial Intelligence},
  year    = {2022},
  number  = {2},
  pages   = {41-53},
  volume  = {62},
}

@article{3,
  title={A neural network to diagnose liver cancer},
  author={Philip S. Maclin and Jack Dempsey},
  journal={IEEE International Conference on Neural Networks},
  year={1993},
  pages={1492-1497},
  volume  = {3}
}

@Article{4,
	author = {Hind Hatem Ramadhan},
	title = {Cerebral Palsy Prediction using CNN Depending on MRI Images of the Brain},
	journal = {Journal of Optoelectronics Laser},
	year = {2022},
	OPTkey = {},
	volume = {41},
	number = {8},
	OPTmonth = {},
	OPTnote = {},
	OPTannote = {},
}

@Article{5,
  author  = {K. Kallianan and J. Mongan and S. Antani and},
  journal = {Clin Radiol},
  title   = {How far have we come? Artificial intelligence for chest radiograph interpretation},
  year    = {2019},
  number  = {5},
  pages   = {pp. 338-345},
  volume  = {74},
}

@Article{6,
	author = {Abhir Bhandary},
	title = {Deep-learning framework to detect lung abnormality – a study with chest X-Ray and lung CT scan images},
	journal = {Pattern Recogn Lett},
	year = {2020},
	OPTkey = {},
	volume = {129},
	pages = {pp. 271-278},
	OPTmonth = {},
	OPTnote = {},
	OPTannote = {},
}

@inproceedings{7,
author = {Nasrullah and Jun Sang and Mohammad S. Alam and Hong Xiang},
title = {{Automated detection and classification for early stage lung cancer on CT images using deep learning}},
volume = {10995},
booktitle = {Pattern Recognition and Tracking XXX},
editor = {Mohammad S. Alam},
organization = {International Society for Optics and Photonics},
publisher = {SPIE},
pages = {109950S},
keywords = {Pulmonary nodules, Computed tomography, Computer-aided detection, Medical image, Deep convolutional neural networks},
year = {2019},
doi = {10.1117/12.2520333},
URL = {https://doi.org/10.1117/12.2520333}
}

@article{8,
title = {Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images},
journal = {Computer Methods and Programs in Biomedicine},
volume = {185},
pages = {105162},
year = {2020},
issn = {0169-2607},
doi = {https://doi.org/10.1016/j.cmpb.2019.105162},
url = {https://www.sciencedirect.com/science/article/pii/S0169260719306960},
author = {Hamed Behzadi-khormouji and Habib Rostami and Sana Salehi and Touba Derakhshande-Rishehri and Marzieh Masoumi and Siavash Salemi and Ahmad Keshavarz and Ali Gholamrezanezhad and Majid Assadi and Ali Batouli},
keywords = {Deep Convolutional Neural Network, Transfer learning, Chest X-ray, Histogram matching, Histogram equalization, Consolidation, Pneumonia, Medical imaging, Pediatric pneumonia, Consolidation},
abstract = {Background and objective
In most patients presenting with respiratory symptoms, the findings of chest radiography play a key role in the diagnosis, management, and follow-up of the disease. Consolidation is a common term in radiology, which indicates focally increased lung density. When the alveolar structures become filled with pus, fluid, blood cells or protein subsequent to a pulmonary pathological process, it may result in different types of lung opacity in chest radiograph. This study aims at detecting consolidations in chest x-ray radiographs, with a certain precision, using artificial intelligence and especially Deep Convolutional Neural Networks to assist radiologist for better diagnosis.
Methods
Medical image datasets usually are relatively small to be used for training a Deep Convolutional Neural Network (DCNN), so transfer learning technique with well-known DCNNs pre-trained with ImageNet dataset are used to improve the accuracy of the models. ImageNet feature space is different from medical images and in the other side, the well-known DCNNs are designed to achieve the best performance on ImageNet. Therefore, they cannot show their best performance on medical images. To overcome this problem, we designed a problem-based architecture which preserves the information of images for detecting consolidation in Pediatric Chest X-ray dataset. We proposed a three-step pre-processing approach to enhance generalization of the models. To demonstrate the correctness of numerical results, an occlusion test is applied to visualize outputs of the model and localize the detected appropriate area. A different dataset as an extra validation is used in order to investigate the generalization of the proposed model.
Results
The best accuracy to detect consolidation is 94.67% obtained by our problem based architecture for the understudy dataset which outperforms the previous works and the other architectures.
Conclusions
The designed models can be employed as computer aided diagnosis tools in real practice. We critically discussed the datasets and the previous works based on them and show that without some considerations the results of them may be misleading. We believe, the output of AI should be only interpreted as focal consolidation. The clinical significance of the finding can not be interpreted without integration of clinical data.}
}

@Article{9,
AUTHOR = {Chouhan, Vikash and Singh, Sanjay Kumar and Khamparia, Aditya and Gupta, Deepak and Tiwari, Prayag and Moreira, Catarina and Damaševičius, Robertas and de Albuquerque, Victor Hugo C.},
TITLE = {A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images},
JOURNAL = {Applied Sciences},
VOLUME = {10},
YEAR = {2020},
NUMBER = {2},
ARTICLE-NUMBER = {559},
URL = {https://www.mdpi.com/2076-3417/10/2/559},
ISSN = {2076-3417},
ABSTRACT = {Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children&rsquo;s Medical Center dataset.},
DOI = {10.3390/app10020559}
}


@INPROCEEDINGS {11,
author = {J. Redmon and S. Divvala and R. Girshick and A. Farhadi},
booktitle = {2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title = {You Only Look Once: Unified, Real-Time Object Detection},
year = {2016},
volume = {},
issn = {1063-6919},
pages = {779-788},
abstract = {We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.},
keywords = {computer architecture;microprocessors;object detection;training;real-time systems;neural networks;pipelines},
doi = {10.1109/CVPR.2016.91},
url = {https://doi.ieeecomputersociety.org/10.1109/CVPR.2016.91},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month = {jun}
}
@article{12,
  author    = {Pranav Rajpurkar and
               Jeremy Irvin and
               Kaylie Zhu and
               Brandon Yang and
               Hershel Mehta and
               Tony Duan and
               Daisy Yi Ding and
               Aarti Bagul and
               Curtis P. Langlotz and
               Katie S. Shpanskaya and
               Matthew P. Lungren and
               Andrew Y. Ng},
  title     = {CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with
               Deep Learning},
  journal   = {CoRR},
  volume    = {abs/1711.05225},
  year      = {2017},
  url       = {http://arxiv.org/abs/1711.05225},
  eprinttype = {arXiv},
  eprint    = {1711.05225},
  timestamp = {Fri, 26 Nov 2021 17:17:06 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1711-05225.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@Inbook{13,
author="{Wang, Xiaosong
and Peng, Yifan
and Lu, Le
and Lu, Zhiyong
and Bagheri, Mohammadhadi
and Summers, Ronald M.}",
//editor="Lu, Le
//and Wang, Xiaosong
//and Carneiro, Gustavo
//and Yang, Lin",
title="ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases",
chapter = {Large-Scale Data Mining and Data Synthesis},
bookTitle="Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics",
year="2019",
publisher="Springer International Publishing",
address="Cham",
pages="369--392",
abstract="The chest X-ray is one of the most commonly accessible radiological examinations for screeningScreening and diagnosis of many lung diseasesLung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are accumulated and stored in many modern hospitals' picture archiving and communication systems (PACS)Picture Archiving and Communication Systems (PACS). On the other side, it is still an open question how this type of hospital-size knowledge database containing invaluable imaging informatics (i.e., loosely labeled) can be used to facilitate the data-hungry deep learning paradigms in building truly large-scaleLarge-scale high-precision computer-aided diagnosis (CAD)Computer-Aided Diagnosis (CAD) systems. In this chapter, we present a chest X-rayChest X-ray database, namely, ``ChestX-ray'', which comprises 121,120 frontal-view X-ray images of 30,805 unique patients with the text-mined eight disease image labels (where each image can have multi-labels), fromMulti-label the associated radiological reports using natural language processingNatural Language Processing (NLP). Importantly, we demonstrate that these commonly occurring thoracic diseasesThoracic disease can be detected and even spatially located via a unified weakly supervisedWeakly supervised multi-labelMulti-label image classificationImage classification and disease localizationDisease localization framework, which is validated using our proposed dataset. Although the initial quantitative results are promising as reported, deep convolutional neural network-based ``reading chest X-rays'' (i.e., recognizing and locating the common disease patterns trained with only image-level labels) remains a strenuous task for fully automated high-precision CADComputer-Aided Diagnosis (CAD) systems.",
isbn="978-3-030-13969-8",
doi="10.1007/978-3-030-13969-8_18",
url="https://doi.org/10.1007/978-3-030-13969-8_18"
}


@article{14,
	title = {PadChest: A large chest x-ray image dataset with multi-label annotated reports},
	journal = {Medical Image Analysis},
	volume = {66},
	pages = {101797},
	year = {2020},
	issn = {1361-8415},
	doi = {https://doi.org/10.1016/j.media.2020.101797},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841520301614},
	author = {Aurelia Bustos and Antonio Pertusa and Jose-Maria Salinas and Maria {de la Iglesia-Vayá}},

}

@Article{15,
	author = {Irvin, J. et al},
	title = {CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison},
	journal = {In Proceedings of the AAAI Conference on Artificial Intelligence},
	year = {2019},
	volume = {33},
	OPTkey = {},
	pages = {590–597},
	OPTmonth = {},
}

@inproceedings{Irvin2019,
	title = "CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison",
	author = {Jeremy Irvin and Pranav Rajpurkar and Michael Ko and Yifan Yu and Silviana Ciurea-Ilcus and Chris Chute and Henrik Marklund and Behzad Haghgoo and Robyn Ball and Katie Shpanskaya and Jayne Seekins and Mong, {David A.} and Halabi, {Safwan S.} and Sandberg, {Jesse K.} and Ricky Jones and Larson, {David B.} and Langlotz, {Curtis P.} and Patel, Bhavik N. and Lungren, {Matthew P.} and Ng, {Andrew Y.}},
	OPTnote = "Publisher Copyright: {\textcopyright} 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Annual Conference on Innovative Applications of Artificial Intelligence, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 ; Conference date: 27-01-2019 Through 01-02-2019",
	year = "2019",
	OPTseries = "33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019",
	publisher = "AAAI Press",
	pages = "590--597",
	booktitle = "33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019",
}

@Article{covid19,
	author = {Pan Y, Guan H, Zhou S et al.},
	title = {Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): a study of 63 patients in Wuhan, China},
	journal = {Eur Radiol.},
	year = {2020},
	volume = {30},
	number = {6},
	OPTkey = {},
	pages = {3306-3309},
	OPTmonth = {},
}

@Article{covid19_2,
	author = {Vasarmidi E, Tsitoura E, Spandidos D, Tzanakis N, Antoniou K.},
	title = {Pulmonary fibrosis in the aftermath of the Covid-19 era (review)},
	journal = {Exp Ther Med.},
	year = {2020},
	OPTkey = {},
	OPTmonth = {},
}



@Book{2022,
  publisher = {Association for Computing Machinery},
  title     = {Proceedings of the 15th International Symposium on Visual Information Communication and Interaction},
  year      = {2022},
  address   = {New York, NY, USA},
  isbn      = {9781450398060},
  location  = {Chur, Switzerland},
}

@Article{16,
  author    = {Alistair E. W. Johnson and Tom J. Pollard and Seth J. Berkowitz and Nathaniel R. Greenbaum and Matthew P. Lungren and Chih-ying Deng and Roger G. Mark and Steven Horng},
  journal   = {CoRR},
  title     = {MIMIC-CXR: A large publicly available database of labeled chest radiographs},
  year      = {2019},
  volume    = {abs/1901.07042},
  bibdate   = {2021-10-14},
  bibsource = {DBLP, http://dblp.uni-trier.de/db/journals/corr/corr1901.html#abs-1901-07042},
  url       = {http://arxiv.org/abs/1901.07042},
}

@Article{Nguyen2022,
  author          = {Nguyen, Ha Q. and Lam, Khanh and Le, Linh T. and Pham, Hieu H. and Tran, Dat Q. and Nguyen, Dung B. and Le, Dung D. and Pham, Chi M. and Tong, Hang T. T. and Dinh, Diep H. and Do, Cuong D. and Doan, Luu T. and Nguyen, Cuong N. and Nguyen, Binh T. and Nguyen, Que V. and Hoang, Au D. and Phan, Hien N. and Nguyen, Anh T. and Ho, Phuong H. and Ngo, Dat T. and Nguyen, Nghia T. and Nguyen, Nhan T. and Dao, Minh and Vu, Van},
  journal         = {Scientific data},
  title           = {VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations.},
  year            = {2022},
  issn            = {2052-4463},
  month           = jul,
  pages           = {429},
  volume          = {9},
  abstract        = {Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs. This limits the development of machine learning algorithms for the detection and localization of chest abnormalities. In this work, we describe a dataset of more than 100,000 chest X-ray scans that were retrospectively collected from two major hospitals in Vietnam. Out of this raw data, we release 18,000 images that were manually annotated by a total of 17 experienced radiologists with 22 local labels of rectangles surrounding abnormalities and 6 global labels of suspected diseases. The released dataset is divided into a training set of 15,000 and a test set of 3,000. Each scan in the training set was independently labeled by 3 radiologists, while each scan in the test set was labeled by the consensus of 5 radiologists. We designed and built a labeling platform for DICOM images to facilitate these annotation procedures. All images are made publicly available in DICOM format along with the labels of both the training set and the test set.},
  citation-subset = {IM},
  completed       = {2022-07-22},
  country         = {England},
  doi             = {10.1038/s41597-022-01498-w},
  issn-linking    = {2052-4463},
  issue           = {1},
  keywords        = {Algorithms; Humans; Mass Chest X-Ray; Radiography; Radiologists; Retrospective Studies},
  nlm-id          = {101640192},
  owner           = {NLM},
  pii             = {429},
  pmc             = {PMC9300612},
  pmid            = {35858929},
  pubmodel        = {Electronic},
  pubstate        = {epublish},
  revised         = {2022-07-25},
}

@Article{Vasarmidi,
  author = {Eirini Vasarmidi and Eliza Tsitoura and Demetrios Spandidos and Nikolaos Tzanakis and Katerina Antoniou},
  title  = {Pulmonary fibrosis in the aftermath of the Covid-19 era (Review)},
  issn   = {1792-0981},
  doi    = {10.3892/etm.2020.8980},
}

@Comment{jabref-meta: databaseType:bibtex;}
