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.

Библиографические подробности
Главные авторы:	Riju, Tanjim Islam, Ramisha, Tahsin Tanim, Aksa, Nusrat Billah, Kabir, Johan H
Другие авторы:	Alam, Md. Golam Rabiul
Формат:	Диссертация
Язык:	English
Опубликовано:	Brac University 2024
Предметы:	CNN State space models MedMamba MedMambaSE Maternal-fetal Artificial intelligence > Medical applications. Fetuses-Ultrasonic imaging. Diagnostic imaging > Digital techniques.
Online-ссылка:	http://hdl.handle.net/10361/24337

id	10361-24337
record_format	dspace
spelling	10361-243372024-10-16T21:01:07Z Fetal plane classification from 2D-ultrasound images leveraging squeeze and excitation self-attention mechanism for feature recalibration in MedMamba Riju, Tanjim Islam Ramisha, Tahsin Tanim Aksa, Nusrat Billah Kabir, Johan H Alam, Md. Golam Rabiul Rahman, Rafeed Department of Computer Science and Engineering, Brac University CNN State space models MedMamba MedMambaSE Maternal-fetal Artificial intelligence--Medical applications. Fetuses-Ultrasonic imaging. Diagnostic imaging--Digital techniques. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 37-40). A fetal ultrasound is a safe pregnancy test that provides an image of the baby’s heart, head, and spine while also analyzing various aspects of its anatomy. Maternal-fetal ultrasound imaging is critical during pregnancy, but existing approaches rely on manual interpretation, which can be time-consuming and can overlook irregularities. Thus, the exploration of fetal ultrasound imaging has resulted in the need for accurate and fast medical image classification. However, there have been some limitations with traditional methods such as Convolutional Neural Networks (CNNs) and transformer models. For example, CNNs do not work well when it comes to modeling long-range dependencies that are very important in medical image feature extraction. Also, transformers have a high quadratic complexity hence demanding too much computation despite being good at dealing with long-range interactions. Our thesis is inspired by recent advances made in state space models (SSM) and thus presents an implementation of Vision Mamba called “MedMamba” designed for classifying medical images. This is achieved through integration of SS-Conv- SSM module which combines local feature extraction capabilities brought about by convolution layers together with long range dependency modeling as exhibited by SSMs thus solving the above mentioned problems encountered during CNN usage. In other words we can say that this hybrid method guarantees strong feature extraction across different types of medical imaging modalities while improving on computational efficiency. Furthermore we have presented an enhancement of the architecture MedMamba called “MedMambaSE” by adding a Squeeze and Excitation (SE) block. This addition refines the process of recalibrating features ultimately improving the models sensitivity and accuracy in detecting abnormalities in development. The incorporation of this block boosts MedMambas adaptability and effectiveness, in handling the complexities of ultrasound images. Through experiments on a dataset of ultrasound images we have shown that MedMambaSE not only enhances classification accuracy but also establishes a new standard for automated analysis of fetal images. This study sets a milestone, in diagnostics and opens doors for advancements in AI driven medical imaging that could revolutionize prenatal care with quicker and more precise interpretations. Tanjim Islam Riju Tahsin Tanim Ramisha Nusrat Billah Aksa Johan H Kabir B.Sc. in Computer Science 2024-10-16T09:06:02Z 2024-10-16T09:06:02Z ©2019 2024-05 Thesis ID 20101403 ID 20101439 ID 22241116 ID 22241114 http://hdl.handle.net/10361/24337 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 49 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	CNN State space models MedMamba MedMambaSE Maternal-fetal Artificial intelligence--Medical applications. Fetuses-Ultrasonic imaging. Diagnostic imaging--Digital techniques.
spellingShingle	CNN State space models MedMamba MedMambaSE Maternal-fetal Artificial intelligence--Medical applications. Fetuses-Ultrasonic imaging. Diagnostic imaging--Digital techniques. Riju, Tanjim Islam Ramisha, Tahsin Tanim Aksa, Nusrat Billah Kabir, Johan H Fetal plane classification from 2D-ultrasound images leveraging squeeze and excitation self-attention mechanism for feature recalibration in MedMamba
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Alam, Md. Golam Rabiul
author_facet	Alam, Md. Golam Rabiul Riju, Tanjim Islam Ramisha, Tahsin Tanim Aksa, Nusrat Billah Kabir, Johan H
format	Thesis
author	Riju, Tanjim Islam Ramisha, Tahsin Tanim Aksa, Nusrat Billah Kabir, Johan H
author_sort	Riju, Tanjim Islam
title	Fetal plane classification from 2D-ultrasound images leveraging squeeze and excitation self-attention mechanism for feature recalibration in MedMamba
title_short	Fetal plane classification from 2D-ultrasound images leveraging squeeze and excitation self-attention mechanism for feature recalibration in MedMamba
title_full	Fetal plane classification from 2D-ultrasound images leveraging squeeze and excitation self-attention mechanism for feature recalibration in MedMamba
title_fullStr	Fetal plane classification from 2D-ultrasound images leveraging squeeze and excitation self-attention mechanism for feature recalibration in MedMamba
title_full_unstemmed	Fetal plane classification from 2D-ultrasound images leveraging squeeze and excitation self-attention mechanism for feature recalibration in MedMamba
title_sort	fetal plane classification from 2d-ultrasound images leveraging squeeze and excitation self-attention mechanism for feature recalibration in medmamba
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/24337
work_keys_str_mv	AT rijutanjimislam fetalplaneclassificationfrom2dultrasoundimagesleveragingsqueezeandexcitationselfattentionmechanismforfeaturerecalibrationinmedmamba AT ramishatahsintanim fetalplaneclassificationfrom2dultrasoundimagesleveragingsqueezeandexcitationselfattentionmechanismforfeaturerecalibrationinmedmamba AT aksanusratbillah fetalplaneclassificationfrom2dultrasoundimagesleveragingsqueezeandexcitationselfattentionmechanismforfeaturerecalibrationinmedmamba AT kabirjohanh fetalplaneclassificationfrom2dultrasoundimagesleveragingsqueezeandexcitationselfattentionmechanismforfeaturerecalibrationinmedmamba
_version_	1814307303586791424

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

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