A comparative analysis of the different CNN-LSTM model caption generation of medical images
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
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| বিন্যাস: | গবেষণাপত্র |
| ভাষা: | English |
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Brac University
2023
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| অনলাইন ব্যবহার করুন: | http://hdl.handle.net/10361/22042 |
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10361-22042 |
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10361-220422023-12-31T21:02:35Z A comparative analysis of the different CNN-LSTM model caption generation of medical images Amin, Mahzabin Yasmin Binte Shammo, Weney Hasan Sayed, Jawad Bin Hossain, MD Junaied Alam, Md. Golam Rabiul Reza, Md. Tanzim Department of Computer Science and Engineering, Brac University Ultrasound image Image captioning Medical image captioning Convolutional Neural Network LSTM Imaging systems in medicine Diagnostic ultrasonic imaging Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-37). The intent of this paper is to make the process of interpreting and understanding information within ultrasound pictures simpler and quicker by addressing the lack of techniques for automatically deciphering medical images. In order to do so, we propose a method of ultrasound image caption generation using AI that highlights the potential Machine Translation has in translating medical images to textual notations. The model needs to be trained on an ultrasound image dataset of the abdominal region including the uterus, myometrium, endometrium and cervix, a field of the medical sector that remains inadequately addressed. Two pre-trained CNN models, namely, VGG16 and Inception v3 have been used to extract features from the ultrasound images. Subsequently, the encoder-decoder model takes in two types of inputs, one for each of its layers. The two kinds of inputs are the text sequence and the image features. Both Vanilla LSTM and Bi-directional LSTM have been used to build the language generation model. The embedding layer along with the LSTM layer will process the text input. At last, the output from the two layers stated above will be merged. Mahzabin Yasmin Binte Amin Weney Hasan Shammo Jawad Bin Sayed MD Junaied Hossain B.Sc. in Computer Science 2023-12-31T05:38:13Z 2023-12-31T05:38:13Z 2023 2023-05 Thesis ID 18101479 ID 19101601 ID 21341025 ID 20101204 http://hdl.handle.net/10361/22042 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 |
Ultrasound image Image captioning Medical image captioning Convolutional Neural Network LSTM Imaging systems in medicine Diagnostic ultrasonic imaging Neural networks (Computer science) |
| spellingShingle |
Ultrasound image Image captioning Medical image captioning Convolutional Neural Network LSTM Imaging systems in medicine Diagnostic ultrasonic imaging Neural networks (Computer science) Amin, Mahzabin Yasmin Binte Shammo, Weney Hasan Sayed, Jawad Bin Hossain, MD Junaied A comparative analysis of the different CNN-LSTM model caption generation of medical images |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. |
| author2 |
Alam, Md. Golam Rabiul |
| author_facet |
Alam, Md. Golam Rabiul Amin, Mahzabin Yasmin Binte Shammo, Weney Hasan Sayed, Jawad Bin Hossain, MD Junaied |
| format |
Thesis |
| author |
Amin, Mahzabin Yasmin Binte Shammo, Weney Hasan Sayed, Jawad Bin Hossain, MD Junaied |
| author_sort |
Amin, Mahzabin Yasmin Binte |
| title |
A comparative analysis of the different CNN-LSTM model caption generation of medical images |
| title_short |
A comparative analysis of the different CNN-LSTM model caption generation of medical images |
| title_full |
A comparative analysis of the different CNN-LSTM model caption generation of medical images |
| title_fullStr |
A comparative analysis of the different CNN-LSTM model caption generation of medical images |
| title_full_unstemmed |
A comparative analysis of the different CNN-LSTM model caption generation of medical images |
| title_sort |
comparative analysis of the different cnn-lstm model caption generation of medical images |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/22042 |
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