An Analysis Conducted Retrospectively on the Use: Artificial Intelligence in the Detection of Uterine Fibroid
- Title
- An Analysis Conducted Retrospectively on the Use: Artificial Intelligence in the Detection of Uterine Fibroid
- Creator
- Kaushik R.; Sharma K.P.; Malik S.; Shanbhog M.
- Description
- The most frequent benign pelvic tumors in women of age of conception are uterine fibroids, sometimes referred to as leiomyomas. Ultrasonography is presently the first imaging modality utilized as clinical identification of uterine fibroids since it has a high degree of specificity and sensitivity and is less expensive and more widely accessible than CT and MRI examination. However, certain issues with ultrasound based uterine fibroid diagnosis persist. The main problem is the misunderstanding of pelvic and adnexal masses, as well as subplasmic and large fibroids. The specificity of fibroid detection is impacted by the existing absence of standardized image capture views and the variations in performance amongst various ultrasound machines. Furthermore, the proficiency and expertise of ultra sonographers determines the accuracy of the ultrasound diagnosis of uterine fibroids. In this work, we created a Deep convolutional neural networks (DCNN) model that automatically identifies fibroids in the uterus in ultrasound pictures, distinguishes between their presence and absence, and has been internally as well as externally validated in order to increase the reliability of the ultrasound examinations for uterine fibroids. Additionally, we investigated whether Deep convolutional neural networks model may help junior ultrasound practitioners perform better diagnostically by comparing it to eight ultrasound practitioners at different levels of experience. 2024 IEEE.
- Source
- 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- DCNN; Deep Learning; Fibroids; Magnetic resonance imaging; Ultrasonography
- Coverage
- Kaushik R., Christ(Deemed to Be Univeristy), Delhi Ncr Campus, Department of Computer Science, Ghaziabad, India; Sharma K.P., Gla University, Department of Computer Engineering and Application, Mathura, India; Malik S., Christ(Deemed to Be Univeristy), Delhi Ncr Campus, Department of Computer Science, Ghaziabad, India; Shanbhog M., Christ(Deemed to Be Univeristy), Delhi Ncr Campus, Department of Computer Science, Ghaziabad, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835035035-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Kaushik R.; Sharma K.P.; Malik S.; Shanbhog M., “An Analysis Conducted Retrospectively on the Use: Artificial Intelligence in the Detection of Uterine Fibroid,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19427.