Revolutionizing Healthcare: An AI-Powered X-ray Analysis App for Fast and Accurate Disease Detection

Revolutionizing Healthcare: An AI-Powered X-ray Analysis App for Fast and Accurate Disease Detection

Authors

  • Shama Kouser jazan university, saudi arabia
  • Anant Aggarwal (anant241203@gmail.com) Research Scientist, Threws, Delhi, India

Keywords:

AI-powered, mobile application, cost-effective, AI

Abstract

This research paper introduces an innovative AI-powered mobile application designed to transform healthcare diagnostics through X-ray analysis. The proposed app employs advanced artificial intelligence and neural networks to analyze X-ray images of various body parts, enabling rapid and precise detection of common diseases and deformities. By eliminating the need for multiple consultations with different specialists, the app offers an accessible and cost-effective solution for individuals to diagnose themselves. Utilizing deep learning techniques, the app performs individualized analyses of each body part, generating comprehensive reports for diseases such as pneumonia, tuberculosis, and osteoporosis, as well as identifying deformities like scoliosis and kyphosis. Its potential to revolutionize medical diagnosis lies in its user-friendly interface, efficiency, and global accessibility, particularly benefiting remote and underserved regions. Results from this study shed light on the immense benefits AI can bring to medical diagnostics, enhancing accuracy while significantly reducing healthcare costs.

References

K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition", Int’l Confer. on Learning Representations, 2015.

V. Badrinarayanan, A. Kendall and R. Cipolla, "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, 2017.

M. Andrew, R. Sanapala, A. Andreyev, H. Bale and C. Hartfield, "Supercharging X-ray Microscopy Using Advanced Algorithms", Microscopy and Analysis, pp. 17, Nov/Dec 2020.

Whig, P., & Ahmad, S. N. (2014). Simulation of linear dynamic macro model of photo catalytic sensor in SPICE. COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering.

Whig, P., Kouser, S., Velu, A., & Nadikattu, R. R. (2022). Fog-IoT-Assisted-Based Smart Agriculture Application. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 74–93). IGI Global.

Whig, P., Velu, A., & Bhatia, A. B. (2022). Protect Nature and Reduce the Carbon Footprint With an Application of Blockchain for IIoT. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 123–142). IGI Global.

Whig, P., Velu, A., & Naddikatu, R. R. (2022). The Economic Impact of AI-Enabled Blockchain in 6G-Based Industry. In AI and Blockchain Technology in 6G Wireless Network (pp. 205–224). Springer, Singapore.

Gu, A. Andreyev, M. Terada, B. Zee, S. M. Zulkifli and Y. Yang, "Accelerate Your 3D X-ray Failure Analysis by Deep Learning High Resolution Reconstruction Paper", Int’l Symp for Testing and Failure Analysis No: istfa2021p0291, pp. 291-295, Dec 2021.

L. Mirkarimi, A. Gu, L. Hunter, G. Guevara, M. Huynh and R. Katkar, "X-ray Microscopy and Root Cause Analysis in Electronic Packaging", Proc 41st Int’l Symp for Testing and Failure Analysis, pp. 430-435, Nov. 2015.

S. M. Zulkifli, B. Zee, W. Qiu and A. Gu, "High-Res 3D X-ray Microscopy for Non-Destructive Failure Analysis of Chip-to-Chip Micro-bump Interconnects in Stacked Die Packages", IEEE 24th Int’l Symp on the Physical and Failure Analysis of Integrated Circuits (IPFA) Chengdu, Jul. 2017.

M. Kaestner, S. Mueller, T. Gregorich, C. Hartfield, C. Nolen and I. Schulmeyer, "Novel Workflow for High-Resolution Imaging of structures in Advanced 3D and Fan-Out Packages", 2019 China Semiconductor Technology International Conference (CSTIC), pp. 1-3, 2019.

Viswanathan and L. Jiao, "Developments in Advanced Packaging Failure Analysis using Correlated X-Ray Microscopy and LaserFIB", 2021 IEEE 23rd Electronics Packaging Technology Conference (EPTC), pp. 80-84, 2021.

Xu X, Jiang X, Ma C, Du P, Li X, Lv S, Yu L, Ni Q, Chen Y, Su J, Lang G, Li Y, Zhao H, Liu J, Xu K, Ruan L, Sheng J, Qiu Y, Wu W, Liang T, Li L (2020) A deep learning system to screen novel coronavirus disease 2019 pneumonia. Engineering 6:1122–1129

El Asnaoui K, ChawkiY (2020) Using X-ray images and deep learning for automated detection of coronavirus disease. J Biomol Struct Dynam 1–12

Ozturk T, Talo M, Yildirim E, Baloglu U, Yildirim O, Rajendra Acharya U (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121-129

Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro P (2020) CovidGAN: data augmentation using auxiliary classifier GAN for improved COVID-19 detection. IEEE Access 8:91916–91923

Whig, P., Velu, A., & Nadikattu, R. R. (2022). Blockchain Platform to Resolve Security Issues in IoT and Smart Networks. In AI-Enabled Agile Internet of Things for Sustainable FinTech Ecosystems (pp. 46–65). IGI Global.

Whig, P., Velu, A., & Ready, R. (2022). Demystifying Federated Learning in Artificial Intelligence With Human-Computer Interaction. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 94–122). IGI Global.

Whig, P., Velu, A., & Sharma, P. (2022). Demystifying Federated Learning for Blockchain: A Case Study. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 143–165). IGI Global.

Chouhan V, Singh S, Khamparia A, Gupta D, Tiwari P, Moreira C, Damaševičius R, de Albuquerque V (2020) A novel transfer learning based approach for pneumonia detection in chest X-ray images. Appl Sci 10:559

Che Azemin M, Hassan R, Mohd Tamrin M, Md Ali M (2020) COVID-19 deep learning prediction model using publicly available radiologist-adjudicated chest X-ray images as training data: preliminary findings. Int J Biomed Imaging 2020:1–7

Ucar F, Korkmaz D (2020) COVIDiagnosis-Net: deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses 140-149

Apostolopoulos I, Aznaouridis S, Tzani M (2020) Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases. J Med Biol Eng 40:462–469

Pereira R, Bertolini D, Teixeira L, Silla C, Costa Y (2020) COVID-19 identification in chest X-ray images on flat and hierarchical classification scenarios. Comput Methods Programs Biomed 194-199.

Whig, P., & Ahmad, S. N. (2012a). A CMOS integrated CC-ISFET device for water quality monitoring. International Journal of Computer Science Issues, 9(4), 1694–1814.

Whig, P., & Ahmad, S. N. (2012f). Performance analysis of various readout circuits for monitoring quality of water using analog integrated circuits. International Journal of Intelligent Systems and Applications, 4(11), 103.

Whig, P., & Ahmad, S. N. (2013a). A novel pseudo-PMOS integrated ISFET device for water quality monitoring. Active and Passive Electronic Components, 2013.

Whig, P., & Ahmad, S. N. (2014a). Development of economical ASIC for PCS for water quality monitoring. Journal of Circuits, Systems and Computers, 23(06), 1450079.

Downloads

Published

2023-06-30

Issue

Section

Articles

How to Cite

Revolutionizing Healthcare: An AI-Powered X-ray Analysis App for Fast and Accurate Disease Detection . (2023). International Journal of Sustainable Development Through AI, ML and IoT, 2(1), 1-23. https://ijsdai.com/index.php/IJSDAI/article/view/15

Similar Articles

11-20 of 28

You may also start an advanced similarity search for this article.