TARU PUBLICATIONS
Journal of Statistics and Management Systems cover

Journal of Statistics and Management Systems

(Since 1998)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510

Indexed in ABDC Australia

Powered by:Powered by

Monthly Journal: Publishes peer-reviewed aticles on theoretical and applied statistics and management systems, expoloring industrial statistics, actuarial and decision sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Statistics and Management Systems
Open Access Research Article

Deep learning for image-based diagnosis : Applications in medical imaging for drug development

* , , , , ,

* Corresponding author · click or hover a name for details

pp. 201–212Vol. 27Issue 2March 2024DOI: 10.47974/JSMS-1247XML
Published Online:
30 Mar 2024
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1247
Pages:
201–212

Abstract

This research delves into the application of Deep Learning (DL) to medical imaging for drug discovery, highlighting DL’s ability to enhance diagnostic precision and efficiency. The research looks into the use of DL methods like CNNs and RNNs for automating feature extraction, pattern recognition, and classification in large, complex medical datasets. Effective drug development relies on fast and accurate diagnosis of disease to determine treatment’s therapeutic efficacy and identify therapeutic targets. When applied to huge datasets, DL excels at uncovering subtle patterns and correlations that may be missed by more conventional methods. In addition to improving early disease identification and tailored medicinal therapies, the technology also helps in biomarker discovery. In this study, we discuss some of the obstacles of deploying DL ethically in medical imaging, such as protecting sensitive patient information, ensuring that models can be easily interpreted, and using a wide variety of data. The merger of DL and medical images offers great potential to promote image-based diagnosis in drug discovery, contributing to a more personalized and precise approach in healthcare, ultimately improving patient outcomes and changing the future of modern medicine.

Keywords

Deep learningCNNRNNMedical imagingMedical image diagnosis

Subject Classifications

68M07

References

[1] P. Xie, T. Li, F. Li, J. Liu, J. Zhou and K. Zuo, “Automated Diagnosis of Melanoma Histopathological Images Based on Deep Learning Using Trust Counting Method,” 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC), Greenville, SC, USA, pp. 26-29 (2021).
[2] N. Araz, G. Orhan, M. Çavuşoğlu, H. E. Surmeli, B. Bayram and T. Çakar, “Development of a Knowledge-Based Multimodal Deep Learning System for Automatic Breast Lesion Segmentation and Diagnosis in MG/DMR Images,” 2023 8th International Conference on Computer Science and Engineering (UBMK), Burdur, Turkiye, pp. 578-583 (2023).
[3] A. A. B. Ramli, Z. Zulkifli, S. Ahmad and N. Ghazali, “Automatic Pneumonia Detection Through Chest X-Ray Image-Based,” 2023 4th International Conference on Artificial Intelligence and Data Sciences (AiDAS), IPOH, Malaysia, pp. 355-360 (2023).
[4] N. Mahajan and H. Chavan, “MRI Images Based Brain Tumor Detection Using CNN for Multiclass Classification,” 2023 3rd Asian Conference on Innovation in Technology (ASIANCON), Ravet IN, India, pp. 1-5 (2023).
[5] A. Mehar, M. Shah and R. Sawant, “Image based Lung Disease Detection: Comparing Swin Transformers and ConvNets,” 2023 3rd Asian Conference on Innovation in Technology (ASIANCON), Ravet IN, India, pp. 1-4 (2023), doi: 10.1109/ASIANCON58793.2023.10270162.
[6] N. Vasker, S. N. Haider, M. Hasan and M. S. Uddin, “Deep Learning-assisted Fracture Diagnosis: Real-time Femur Fracture Diagnosis and Categorization,” 2023 4th International Conference on Big Data Analytics and Practices (IBDAP), Bangkok, Thailand, pp. 1-6 (2023).
[7] S. M. McKinney et al., “A Deep Learning Framework for Robust and Scalable Breast Cancer Screening”, Nature, vol. 577, no. 7788, pp. 89-94, Jan. (2020).
[8] S. Han et al., “Deep Learning-Based Classification of Breast Tumors Using 3D Ultrasound Images”, Ultrasound in Medicine & Biology, vol. 46, no. 6, pp. 1442-1452, Jun. (2020).
[9] N. Dhungel et al., “Deep Learning-Based Classification of Mammographic Regions of Interest Towards Breast Mass Detection”, Medical Image Analysis, vol. 37, pp. 62-73, Jul. (2017).
[10] M. Bende, M. Khandelwal, D. Borgaonkar and P. Khobragade, “VISMA: A Machine Learning Approach to Image Manipulation,” 2023 6th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, pp. 1-5 (2023).
[11] A. Rodríguez-Ruiz et al., “Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer”, Investigative Radiology, vol. 54, no. 4, pp. 221-227, Apr. (2019).
[12] A. Rodríguez-Ruiz et al., “Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening”, IEEE Transactions on Medical Imaging, vol. 38, no. 10, pp. 2638-2648, Oct. (2019).
[13] S. Haq et al., “Deep Learning in Breast Cancer Detection: A Comparative Study”, IEEE Access, vol. 8, pp. 194760-194770 (2020).
[14] M. H. Yap et al., “Deep Learning Models for Breast Cancer Detection and Diagnosis: A Review”, Neurocomputing, vol. 440, pp. 11-24, Feb. (2021).

Views: 243Downloads: 75Citations: 0