Thesis (Bachelor of Science in Computer Science)

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    Semantic segmentation with attention dense U-net for lung extraction from X-ray images
    (BRAC University, 2023-03) Auvy, Akib Al Mahmud; Sharif, Shezhan; Chowdhury, Aseer Iqtider; Elahi, Mahbub-E; Mahmud, Washik Al; Noor, Jannatun
    "In the diverse field of computer science, deep learning and digital image processing plays a vital role in medical image research. With a deep knowledge on hand, we can make a machine understand any medical documentation, and fourth, image segmentation, classification, detection, identification, and segmentation have become more reliable and precious by the day. Lung disease detection is one of the most challenging parts of automation machine detection; to achieve that, segmentation is vigorous. For our research purpose, we aim to seek a better-unused model for lung segmentation, and it is fruitless to justify all the deep learning model as the number is huge. Most of them has already been evaluated by another researcher. This is why we have used U-net architecture (Attention Dense U-Net, Dense U- Net, Attention U-Net, U-Net, U-Net++) to segment the lung from an X-ray image. For the named architecture, we have used two convolutional layers. Four types of accuracy measurement matrices were used to judge this U-Net model: accuracy, Dice coefficient, intersection over union(IoU), and validation loss. The milestone for our research is as follows: our collected dataset was originally 512 x 512 pixels which we converted to 256 x 256 pixels for a 2 x 2 patch. This enables the machine to read the image with a better result. The dataset is annotated and masked. After that, we performed deep learning of the U-Net structure for our dataset to train the U-Net model and segment lung pixels from an X-ray image. In this step, we first omitted the background from the image with the help of true positive, true negative, false positive, and false negative values. Finally, we measured our model accuracy by the advanced accuracy measurement algorithm to justify its capability in terms of unknown data. Following these three steps, we have found that Attention Dense U-Net gives the best accuracy for all given parameters, with the result of accuracy: 97.48% Dice coefficient: 94.87% IoU: 93.87%. And the lowest is base U-Net with an Accuracy score: of 96.68%, Dice Coefficient: of 92.1% IoU: of 91.75%. The study reflects that U-Net is unsuitable for the segmentation of lungs from X-ray images. Hence, we have suggested our approach with Attention Dense U-Net for lung segmentation."
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    Ejection fraction estimation using deep semantic segmentation neural network on 2D Echocardiography data
    (BRAC University, 2020-04) Khan, Abde Musavvir; Shejuty, Myesha Farid; Talukder, MD. Nafis Shariar; Zubayear, Syed Ibna; Alam, Md. Golam Rabiul
    Ejection fraction value denotes how much blood is pumped out of the heart to different parts of the body. It is a routine clinical procedure in heart function assessment, where the left ventricle of the heart has to be manually outlined by doctors in clinical settings to measure the EF value which is time consuming and highly varies by observer. Modern day deep learning methods are able to automatically complete this type of outlining task automatically with much ease and better efficiency even when the model is trained on a deeper neural network and smaller dataset. This paper investigates the deep semantic segmentation networks to find the most accurate one to implement an EF estimation system could be built on the most accurate image segmentation network which will reduce the pressure off the doctors shoulders and stop the eyeball estimation of EF values which is subject to inter-observer variability. This paper evaluated three different image segmentation neural networks namely U-Net, ResUNet, Deep ResUNet to find their accuracy score basing mostly on the dice accuracy metric. The most accurate model of the three Deep ResUNet has been utilized to form Left Ventricle segmentation network for end systole and end diastole images on which volume measurement formula is applied to find out the Ejection Fraction value.