Published on in Vol 22, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18563, first published .
Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation

Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation

Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation

Journals

  1. Attallah O, Sharkas M. GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases. PeerJ Computer Science 2021;7:e423 View
  2. Shen T, Li X. Automatic polyp image segmentation and cancer prediction based on deep learning. Frontiers in Oncology 2023;12 View
  3. Tufail A, Ma Y, Kaabar M, Martínez F, Junejo A, Ullah I, Khan R, Liao I. Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. Computational and Mathematical Methods in Medicine 2021;2021:1 View
  4. Guo X, Zhang L, Hao Y, Zhang L, Liu Z, Liu J. Multiple abnormality classification in wireless capsule endoscopy images based on EfficientNet using attention mechanism. Review of Scientific Instruments 2021;92(9) View
  5. Arsalan M, Haider A, Won Lee Y, Ryoung Park K. Detecting retinal vasculature as a key biomarker for deep Learning-based intelligent screening and analysis of diabetic and hypertensive retinopathy. Expert Systems with Applications 2022;200:117009 View
  6. Peyret R, alSaeed D, Khelifi F, Al-Ghreimil N, Al-Baity H, Bouridane A. Convolutional Neural Network–Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System Development Study. JMIR Bioinformatics and Biotechnology 2022;3(1):e27394 View
  7. Dureja A, Pahwa P. Integrating CNN along with FAST descriptor for accurate retrieval of medical images with reduced error probability. Multimedia Tools and Applications 2023;82(12):17659 View
  8. Arsalan M, Haider A, Choi J, Park K. Diabetic and Hypertensive Retinopathy Screening in Fundus Images Using Artificially Intelligent Shallow Architectures. Journal of Personalized Medicine 2021;12(1):7 View
  9. Arsalan M, Haider A, Koo J, Park K. Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis. Mathematics 2022;10(9):1536 View
  10. Haider A, Arsalan M, Park C, Sultan H, Park K. Exploring deep feature-blending capabilities to assist glaucoma screening. Applied Soft Computing 2023;133:109918 View
  11. Shi J, Zhang Q, Tang Y, Zhang Z. Polyp-Mixer: An Efficient Context-Aware MLP-Based Paradigm for Polyp Segmentation. IEEE Transactions on Circuits and Systems for Video Technology 2023;33(1):30 View
  12. Parkash O, Siddiqui A, Jiwani U, Rind F, Padhani Z, Rizvi A, Hoodbhoy Z, Das J. Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis. Frontiers in Medicine 2022;9 View
  13. Parkash O, Saleha Siddiqui A, Jiwani U, Rind F, Padhani Z, Rizvi A, Hoodbhoy Z, Das J. Diagnostic Accuracy of Artificial Intelligence for Detecting Gastroenterological Pathologies: A Systematic Review and Meta-Analysis. SSRN Electronic Journal 2022 View
  14. Feng Y, Cong Y, Xing S, Wang H, Zhao C, Zhang X, Yao Q. Distance Matters: A Distance-Aware Medical Image Segmentation Algorithm. Entropy 2023;25(8):1169 View
  15. Farhad M, Masud M, Beg A, Ahmad A, Ahmed L. A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques. Applied Sciences 2023;13(11):6582 View

Books/Policy Documents

  1. Ali M, Li C, He K. Biometric Recognition. View