3 research outputs found
Classification of breast cancer grades using physical parameters and K-nearest neighbor method
Breast cancer is a health problem in the world. To overcome this problem requires early detection of breast cancer. The purpose of this study is to classify early breast cancer grades. Combination of physical parameters with k-nearest neighbor Method is proposed to detect early breast cancer grades. The experiments were performed on 87 mammograms consisting of 12 mammograms of grade 1,41 mammograms of grade 2 and 34 mammogram of grade 3. The proposed method was effective to classify the grades of breast cancer by an accuracy of 64.36%, 50% sensitivity and 73,5% specitifity. Physical parameters can be used to classify grades of breast cancer. The results of this study can be used to complement the diagnosis of breast mammography examination
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Pattern classification approaches for breast cancer identification via MRI: stateāofātheāart and vision for the future
Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI)
of breast tissue are discussed. The algorithms are based on recent advances in multidimensional
signal processing and aim to advance current stateāofātheāart computerāaided detection
and analysis of breast tumours when these are observed at various states of development. The topics
discussed include image feature extraction, information fusion using radiomics, multiāparametric
computerāaided classification and diagnosis using information fusion of tensorial datasets as well
as Clifford algebra based classification approaches and convolutional neural network deep learning
methodologies. The discussion also extends to semiāsupervised deep learning and selfāsupervised
strategies as well as generative adversarial networks and algorithms using generated
confrontational learning approaches. In order to address the problem of weakly labelled tumour
images, generative adversarial deep learning strategies are considered for the classification of
different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence
(AI) based framework for more robust image registration that can potentially advance the early
identification of heterogeneous tumour types, even when the associated imaged organs are
registered as separate entities embedded in more complex geometric spaces. Finally, the general
structure of a highādimensional medical imaging analysis platform that is based on multiātask
detection and learning is proposed as a way forward. The proposed algorithm makes use of novel
loss functions that form the building blocks for a generated confrontation learning methodology
that can be used for tensorial DCEāMRI. Since some of the approaches discussed are also based on
timeālapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The
proposed framework can potentially reduce the costs associated with the interpretation of medical
images by providing automated, faster and more consistent diagnosis