MobileNetV3: an efficient deep learning-based feature selection and classification technique for cardiovascular disease
Abstract Accurately identifying cardiovascular illness is one of the most important and difficult responsibilities in treating a patient before a heart attack. Unfortunately, most currently utilized cardiovascular disease prediction algorithms could not achieve higher accuracy due to inadequate fore...
Saved in:
| Main Authors: | B. Dhanalaxmi, B. Naveen Kumar, Yeligeti Raju, Rama Seshagiri Rao Channapragada |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
|
| Series: | Journal of Engineering and Applied Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s44147-025-00654-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing glaucoma diagnosis: Generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2
by: I. Govindharaj, et al.
Published: (2025-06-01) -
Multi-Class Brain Lesion Classification Using Deep Transfer Learning With MobileNetV3
by: Ahmed Firas Majeed, et al.
Published: (2024-01-01) -
Lightweight concrete crack recognition model based on improved MobileNetV3
by: Rui Wang, et al.
Published: (2025-05-01) -
A Combined MobileNetV2 and CBAM Model to Improve Classifying the Breast Cancer Ultrasound Images
by: Muhammad Rakha, et al.
Published: (2024-12-01) -
DSS-MobileNetV3: An Efficient Dynamic-State-Space- Enhanced Network for Concrete Crack Segmentation
by: Haibo Li, et al.
Published: (2025-06-01)