@article{ismael_enhanced_2020,
	title = {An enhanced deep learning approach for brain cancer {MRI} images classification using residual networks},
	volume = {102},
	issn = {0933-3657},
	url = {http://www.sciencedirect.com/science/article/pii/S0933365719306177},
	doi = {https://doi.org/10.1016/j.artmed.2019.101779},
	abstract = {Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Over recent years, deep learning has shown an optimistic performance in computer vision systems. In this paper, we propose an enhanced approach for classifying brain tumor types using Residual Networks. We evaluate the proposed model on a benchmark dataset containing 3064 MRI images of 3 brain tumor types (Meningiomas, Gliomas, and Pituitary tumors). We have achieved the highest accuracy of 99\% outperforming the other previous work on the same dataset.},
	journal = {Artificial Intelligence in Medicine},
	author = {Ismael, Sarah Ali [Abdelaziz and Mohammed, Ammar and Hefny, Hesham},
	year = {2020},
	keywords = {Artificial neural network, Cancer classification, Convolutional neural network, Deep residual network, Machine learning},
	pages = {101779},
}

{"_id":"3DuZtkTrnqiDGRANt","bibbaseid":"ismael-mohammed-hefny-anenhanceddeeplearningapproachforbraincancermriimagesclassificationusingresidualnetworks-2020","authorIDs":[],"author_short":["Ismael, S. A. [.","Mohammed, A.","Hefny, H."],"bibdata":{"bibtype":"article","type":"article","title":"An enhanced deep learning approach for brain cancer MRI images classification using residual networks","volume":"102","issn":"0933-3657","url":"http://www.sciencedirect.com/science/article/pii/S0933365719306177","doi":"https://doi.org/10.1016/j.artmed.2019.101779","abstract":"Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Over recent years, deep learning has shown an optimistic performance in computer vision systems. In this paper, we propose an enhanced approach for classifying brain tumor types using Residual Networks. We evaluate the proposed model on a benchmark dataset containing 3064 MRI images of 3 brain tumor types (Meningiomas, Gliomas, and Pituitary tumors). We have achieved the highest accuracy of 99% outperforming the other previous work on the same dataset.","journal":"Artificial Intelligence in Medicine","author":[{"propositions":[],"lastnames":["Ismael"],"firstnames":["Sarah","Ali","[Abdelaziz"],"suffixes":[]},{"propositions":[],"lastnames":["Mohammed"],"firstnames":["Ammar"],"suffixes":[]},{"propositions":[],"lastnames":["Hefny"],"firstnames":["Hesham"],"suffixes":[]}],"year":"2020","keywords":"Artificial neural network, Cancer classification, Convolutional neural network, Deep residual network, Machine learning","pages":"101779","bibtex":"@article{ismael_enhanced_2020,\n\ttitle = {An enhanced deep learning approach for brain cancer {MRI} images classification using residual networks},\n\tvolume = {102},\n\tissn = {0933-3657},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0933365719306177},\n\tdoi = {https://doi.org/10.1016/j.artmed.2019.101779},\n\tabstract = {Cancer is the second leading cause of death after cardiovascular diseases. Out of all types of cancer, brain cancer has the lowest survival rate. Brain tumors can have different types depending on their shape, texture, and location. Proper diagnosis of the tumor type enables the doctor to make the correct treatment choice and help save the patient's life. There is a high need in the Artificial Intelligence field for a Computer Assisted Diagnosis (CAD) system to assist doctors and radiologists with the diagnosis and classification of tumors. Over recent years, deep learning has shown an optimistic performance in computer vision systems. In this paper, we propose an enhanced approach for classifying brain tumor types using Residual Networks. We evaluate the proposed model on a benchmark dataset containing 3064 MRI images of 3 brain tumor types (Meningiomas, Gliomas, and Pituitary tumors). We have achieved the highest accuracy of 99\\% outperforming the other previous work on the same dataset.},\n\tjournal = {Artificial Intelligence in Medicine},\n\tauthor = {Ismael, Sarah Ali [Abdelaziz and Mohammed, Ammar and Hefny, Hesham},\n\tyear = {2020},\n\tkeywords = {Artificial neural network, Cancer classification, Convolutional neural network, Deep residual network, Machine learning},\n\tpages = {101779},\n}\n\n","author_short":["Ismael, S. A. [.","Mohammed, A.","Hefny, H."],"key":"ismael_enhanced_2020","id":"ismael_enhanced_2020","bibbaseid":"ismael-mohammed-hefny-anenhanceddeeplearningapproachforbraincancermriimagesclassificationusingresidualnetworks-2020","role":"author","urls":{"Paper":"http://www.sciencedirect.com/science/article/pii/S0933365719306177"},"keyword":["Artificial neural network","Cancer classification","Convolutional neural network","Deep residual network","Machine learning"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/zotero/m.shamimkaiser","creationDate":"2021-02-19T18:04:08.987Z","downloads":0,"keywords":["artificial neural network","cancer classification","convolutional neural network","deep residual network","machine learning"],"search_terms":["enhanced","deep","learning","approach","brain","cancer","mri","images","classification","using","residual","networks","ismael","mohammed","hefny"],"title":"An enhanced deep learning approach for brain cancer MRI images classification using residual networks","year":2020,"dataSources":["yda423vBhGBN7Q3WB"]}