Fetal plane classification from 2D-ultrasound images leveraging squeeze and excitation self-attention mechanism for feature recalibration in MedMamba

Fetal plane classification from 2D-ultrasound images leveraging squeeze and excitation self-attention mechanism for feature recalibration in MedMamba

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Bibliographische Detailangaben
Hauptverfasser: Riju, Tanjim Islam, Ramisha, Tahsin Tanim, Aksa, Nusrat Billah, Kabir, Johan H
Weitere Verfasser: Alam, Md. Golam Rabiul
Format: Abschlussarbeit
Sprache:English
Veröffentlicht: Brac University 2024
Schlagworte:
CNN
State space models
MedMamba
MedMambaSE
Maternal-fetal
Artificial intelligence > Medical applications.
Fetuses-Ultrasonic imaging.
Diagnostic imaging > Digital techniques.
Online Zugang:http://hdl.handle.net/10361/24337

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