Advancing multi-categorization and segmentation in brain tumors using novel efficient deep learning approaches
Nadenlla RajamohanReddy, G. Muneeswari · 2024
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Description
RajamohanReddy and Muneeswari present a 2024 deep learning system for brain tumor scans. Tumors vary in size, shape, and seat, which strains older sorting tools and eats time. Their system must do three jobs: say if a tumor is there, name its type, and outline the sick zone.
Their network pairs a stretched residual design with a channel attention step that weighs the most telling features. It sorts scans into four bins: pituitary tumor, glioma, meningioma, or no tumor. Trained on MRI scans, the design aims for sharper calls and cleaner outlines than past models, to aid diagnosis and treatment planning.
Catalog record
| CREATOR | Nadenlla RajamohanReddy, G. Muneeswari |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 104 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |