Cancer Classification with Deep Learning using Genomics Data

Ahsan, Ahmad Omar; Ansar, Md. Azmaeen Bin; Minhaj, Minhajul Islam

dc.contributor.author	Ahsan, Ahmad Omar
dc.contributor.author	Ansar, Md. Azmaeen Bin
dc.contributor.author	Minhaj, Minhajul Islam
dc.date.accessioned	2022-03-28T06:06:20Z
dc.date.available	2022-03-28T06:06:20Z
dc.date.issued	2021-03-30
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dc.identifier.uri	http://hdl.handle.net/123456789/1294
dc.description	Supervised by Mr. Tareque Mohmud Chowdhury, Assistant Professor, Co-supervisor Mr. Tasnim Ahmed Lecturer, Department of CSE, Islamic University of Technology, Gazipur, Bangladesh.	en_US
dc.description.abstract	Deep learning has been monumental in Computer Vision, Natural Language Processing, Machine Translation task and so on. In bioinformatics, Deep learning is playing an important role in drug discover and protein structure prediction. In cancer diagnosis, thanks to advances in Computer Vision Deep Learning models are able to accurately classify cancer. However, not much work has been done in the field of Cancer diagnosis with genomic data. Several authors attempted to use genomic data using machine learning, however it was restricted to single cancer subtypes. In this thesis, we explored classification of all types of cancer using miRNA genome data by creating new model architectures. We are proposing two new architectures a basic ANN and a novel architecture based on ResNet called CResNet. We have trained 4 different kinds of model. LSTM, Artificial Neural Network, CResNet (Variant of ResNet Architecture) and Ensemble models using model averaging. Our models have achieved MCC (Mathew’s correlation coefficient) value of 0.8596,0.9625,0.9745 and 0.9439 which is greater than the SOTA model’s MCC model demonstrating that our architecture performed better than the current architecture.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Cancer Classification with Deep Learning using Genomics Data	en_US
dc.type	Thesis	en_US