scholarly journals Novel Supervised Learning Scheme for Optimizing the Classification Performance of Breast Cancer MRI

2020 ◽  
Vol 9 (5) ◽  
pp. 1144-1151
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Supervised Learning ◽  
Complex Disease ◽  
Classification Performance ◽  
Learning Approach ◽  
Extraction Scheme ◽  
Magnetic Resonance Imaging Mri ◽  
Breast Cancer Mri

Usage of machine learning has been always proven potential in identifying the best solution from the set of complex variables with the highly inter-twined relationship of problems. Similarly, supervised learning approach is one essential operation under machine learning that has always contributed in the area of healthcare and diagnostics. However, there are still some problems associated with the detection and classification of complex disease condition that is yet to be solved. The proposed system introduces a novel supervised learning approach along with a novel feature extraction scheme which is more progressive and less iterative. The proposed system considers a case study to perform classification of breast cancer using Magnetic Resonance Imaging (MRI) where it is subjected to normalization first followed by a novel segmentation process that compliments the classification operation too. The study outcome shows that the proposed system offers better classification performance in contrast to existing supervised approaches.

scholarly journals Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4373 ◽  
Author(s):  
Zabit Hameed ◽  
Sofia Zahia ◽  
Begonya Garcia-Zapirain ◽  
José Javier Aguirre ◽  
Ana María Vanegas
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Diagnostic System ◽  
Microscopic Analysis ◽  
Classification Performance ◽  
Histopathological Analysis ◽  
Learning Approach ◽  
Ensemble Strategy ◽  
Histopathology Images

Breast cancer is one of the major public health issues and is considered a leading cause of cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing the chances of survival rate. To this end, biopsy is usually followed as a gold standard approach in which tissues are collected for microscopic analysis. However, the histopathological analysis of breast cancer is non-trivial, labor-intensive, and may lead to a high degree of disagreement among pathologists. Therefore, an automatic diagnostic system could assist pathologists to improve the effectiveness of diagnostic processes. This paper presents an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma breast cancer histopathology images using our collected dataset. We trained four different models based on pre-trained VGG16 and VGG19 architectures. Initially, we followed 5-fold cross-validation operations on all the individual models, namely, fully-trained VGG16, fine-tuned VGG16, fully-trained VGG19, and fine-tuned VGG19 models. Then, we followed an ensemble strategy by taking the average of predicted probabilities and found that the ensemble of fine-tuned VGG16 and fine-tuned VGG19 performed competitive classification performance, especially on the carcinoma class. The ensemble of fine-tuned VGG16 and VGG19 models offered sensitivity of 97.73% for carcinoma class and overall accuracy of 95.29%. Also, it offered an F1 score of 95.29%. These experimental results demonstrated that our proposed deep learning approach is effective for the automatic classification of complex-natured histopathology images of breast cancer, more specifically for carcinoma images.

scholarly journals A machine learning approach to predict healthcare cost of breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pratyusha Rakshit ◽  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez-Inhiesto ◽  
Maria T. Acaiturri-Ayesta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Healthcare Cost ◽  
Percentage Error ◽  
Learning Approach ◽  
Early Prediction ◽  
Breast Cancer Patients ◽  
Machine Learning Approach ◽  
Clinical Records

AbstractThis paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.

scholarly journals A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

2021 ◽  
Vol 9 (5) ◽  
pp. 1034
Author(s):  
Carlos Sabater ◽  
Lorena Ruiz ◽  
Abelardo Margolles
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Human Milk Oligosaccharides ◽  
Future Studies ◽  
High Fiber ◽  
Machine Learning Approach ◽  
Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

scholarly journals A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

2020 ◽  
pp. 107151
Author(s):  
Alexis Falcin ◽  
Jean-Philippe Métaxian ◽  
Jérôme Mars ◽  
Éléonore Stutzmann ◽  
Jean-Christophe Komorowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Classification ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Volcanic Seismicity
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