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Classification of Sub-type of Lymphoma using Deep Learning

Thorat, Prasad Balasaheb (2020) Classification of Sub-type of Lymphoma using Deep Learning. Masters thesis, Dublin, National College of Ireland.

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The accurate identification and categorization of cancer structure or sub-type is an important task because of considerable workload and expertise in pathological skills. The current digitization in diagnostic system of disease provides huge quantities of image data enabling a faster and accurate diagnosis through the development of automatic techniques for image classification. Previous studies have provided evidence for automatic cancer tissue analysis by using deep learning strategies that retrieve and organize discriminating insights from the images automatically. Therefore, in this study an innovative and empowered deep learning framework is proposed to classify three types of lymphoma as Follicular Lymphoma (FL), Chronic Lymphocytic Lymphoma (CLL) and Mantle Cell Lymphoma (MCL). In this research, a technique is followed with the help of the Inception V3 and DenseNet121 along with a CNN. A publicly available dataset from National Institute of Ageing (NIA) is used in this study. This study performs the histogram normalization on all the images to enhance the performance of model. The data augmentation have been carried out on the dataset so that overfitting can be avoided. The achieved accuracy 92.51% from study shows that Inception v3 managed to achieved better results in comparison with DenseNet121 and CNN in terms of categorical accuracy.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 25 Jan 2021 15:50
Last Modified: 25 Jan 2021 15:50

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