Cancer diagnosis using deep learning: a bibliographic review
In this paper, we first describe the basics of the field of cancer diagnosis, which includes
steps of cancer diagnosis followed by the typical classification methods used by doctors …
steps of cancer diagnosis followed by the typical classification methods used by doctors …
Ensemble learning: A survey
O Sagi, L Rokach - Wiley interdisciplinary reviews: data mining …, 2018 - Wiley Online Library
Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …
challenges. Such methods improve the predictive performance of a single model by training …
Deep learning for image-based cancer detection and diagnosis− A survey
In this paper, we aim to provide a survey on the applications of deep learning for cancer
detection and diagnosis and hope to provide an overview of the progress in this field. In the …
detection and diagnosis and hope to provide an overview of the progress in this field. In the …
Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks
A major challenge in brain tumor treatment planning and quantitative evaluation is
determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) …
determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) …
Brain tumor segmentation using convolutional neural networks in MRI images
Among brain tumors, gliomas are the most common and aggressive, leading to a very short
life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the …
life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the …
A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned
The successful early diagnosis of brain tumors plays a major role in improving the treatment
outcomes and thus improving patient survival. Manually evaluating the numerous magnetic …
outcomes and thus improving patient survival. Manually evaluating the numerous magnetic …
Exploring task structure for brain tumor segmentation from multi-modality MR images
Brain tumor segmentation, which aims at segmenting the whole tumor area, enhancing
tumor core area, and tumor core area from each input multi-modality bio-imaging data, has …
tumor core area, and tumor core area from each input multi-modality bio-imaging data, has …
Brain tumor detection and segmentation in MR images using deep learning
S Sajid, S Hussain, A Sarwar - Arabian Journal for Science and …, 2019 - Springer
Gliomas are the most infiltrative and life-threatening brain tumors with exceptionally quick
development. Gliomas segmentation using computer-aided diagnosis is a challenging task …
development. Gliomas segmentation using computer-aided diagnosis is a challenging task …
Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI
Purpose We propose a fully automated method for detection and segmentation of the
abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid …
abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid …
Brain tumor detection: a long short-term memory (LSTM)-based learning model
To overcome the problems of automated brain tumor classification, a novel approach is
proposed based on long short-term memory (LSTM) model using magnetic resonance …
proposed based on long short-term memory (LSTM) model using magnetic resonance …