Automated detection of Malignant Lesions in the ovary using deep learning models and XAI

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:	Ifty, Md. Hasin Sarwar, Nirjan, Nisharga, Diganta, M.A., Islam, Labib, Ornate, Reeyad Ahmed
Другие авторы:	Islam, Md. Saiful
Формат:	Диссертация
Язык:	English
Опубликовано:	Brac University 2024
Предметы:	Ovarian cancer Convolutional neural network Deep learning XAI Disease detection Neural networks (Computer science) Artificial intelligence Deep learning (Machine learning)
Online-ссылка:	http://hdl.handle.net/10361/22877

id	10361-22877
record_format	dspace
spelling	10361-228772024-10-01T08:37:07Z Automated detection of Malignant Lesions in the ovary using deep learning models and XAI Ifty, Md. Hasin Sarwar Nirjan, Nisharga Diganta, M.A. Islam, Labib Ornate, Reeyad Ahmed Islam, Md. Saiful Tasnim, Anika Department of Computer Science and Engineering, Brac University Ovarian cancer Convolutional neural network Deep learning XAI Disease detection Neural networks (Computer science) Artificial intelligence Deep learning (Machine learning) 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 39-42). Cancer is a complex and highly invasive disease that forms due to the abnormal growth of cells in any part of the body. A majority of cancers are unraveled and treated by incorporating advanced technology. However, ovarian cancer remains a dilemma as it has inaccurate non-invasive detection and a time consuming and invasive procedure for accurate detection. Medical professionals are constantly acquiring enhanced diagnostic and treatment abilities by implementing deep learning models to analyze medical data for better clinical decision, disease diagnosis and drug discovery. Thus, in this research, several Convolutional Neural Networks such as LeNet-5, ResNet, VGGNet and GoogLeNet/Inception have been utilized to develop a model that accurately detects and identifies ovarian cancer. For effective model training, the dataset OvarianCancer&SubtypesDatasetHistopathology from Mendeley has been used. After selecting a base model, we utilized XAI models such as LIME, Integrated Gradients and SHAP to explain the black box outcome of the selected model. For evaluating the performance of the base model, Accuracy, Precision, Recall, F1-Score and ROC Curve/AUC have been used. From the evaluation, it was seen that the slightly compact InceptionV3 model with ReLu had the overall best result achieving an average score of 94% across the performance metrics in the augmented dataset. Lastly for XAI, the three aforementioned XAI have been used for an overall comparative analysis. It is the aim of this research that the contributions of the study will help in achieving a better detection method for ovarian cancer. Md. Hasin Sarwar Ifty Nisharga Nirjan M.A. Diganta Labib Islam Reeyad Ahmed Ornate B.Sc. in Computer Science 2024-05-19T10:42:00Z 2024-05-19T10:42:00Z ©2024 2024-01 Thesis ID: 20101017 ID: 20101020 ID: 20101034 ID: 20101039 ID: 23141041 http://hdl.handle.net/10361/22877 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. 52 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Ovarian cancer Convolutional neural network Deep learning XAI Disease detection Neural networks (Computer science) Artificial intelligence Deep learning (Machine learning)
spellingShingle	Ovarian cancer Convolutional neural network Deep learning XAI Disease detection Neural networks (Computer science) Artificial intelligence Deep learning (Machine learning) Ifty, Md. Hasin Sarwar Nirjan, Nisharga Diganta, M.A. Islam, Labib Ornate, Reeyad Ahmed Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
author2	Islam, Md. Saiful
author_facet	Islam, Md. Saiful Ifty, Md. Hasin Sarwar Nirjan, Nisharga Diganta, M.A. Islam, Labib Ornate, Reeyad Ahmed
format	Thesis
author	Ifty, Md. Hasin Sarwar Nirjan, Nisharga Diganta, M.A. Islam, Labib Ornate, Reeyad Ahmed
author_sort	Ifty, Md. Hasin Sarwar
title	Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
title_short	Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
title_full	Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
title_fullStr	Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
title_full_unstemmed	Automated detection of Malignant Lesions in the ovary using deep learning models and XAI
title_sort	automated detection of malignant lesions in the ovary using deep learning models and xai
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22877
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_version_	1814307588727111680

Automated detection of Malignant Lesions in the ovary using deep learning models and XAI

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