Automatic segmentation and classification of breast masses from mammography using machine learning

2021 ◽  
Author(s):  
Prerna Chander ◽  
Lina Eilouti ◽  
Saubia Khan ◽  
Salam Dhou ◽  
Michel Pasquier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Segmentation ◽  
Breast Masses

scholarly journals Automated Detection and Classification of Ki-67 Stained Nuclear Section Using Machine Learning Based on Texture of Nucleus to Measure Proliferation Score for Prognostic Evaluation of Breast Carcinoma

2020 ◽  
Author(s):  
Anil Kumar ◽  
Manish Prateek
Keyword(s):  
Machine Learning ◽  
Breast Carcinoma ◽  
Automatic Segmentation ◽  
Blue Color ◽  
Ki 67 ◽  
Data Set ◽  
Unsupervised Machine Learning ◽  
Histopathological Images ◽  
Hematoxylin And Eosin

Abstract Background: This study aimed significance of Ki-67 labels and calculated the proliferation score based on the counting of immunopositive and immunonegative nuclear sections with the help of machine learning to predict the intensity of breast carcinoma.Methods: BreCaHAD (Breast Cancer Histopathological Annotation and Diagnosis) dataset includes various malignant cases of different patients in their routine diagnosis. It contains H&E stained microscopic histopathological images at 40x magnification and stored in .tiff format using RGB band. In this study, the method start with preprocessing that focuses on resizing, smoothing and enhancement. After preprocessing, it is decomposed RGB sample into HSI values. BreCaHAD data set is hematoxylin and eosin (H&E) stained, where brown and blue color level have a major role to differentiate the immunopositive and immunonegative nuclear sections. Blue color in RGB and Hue in HSI are the intrinsic characteristic of H&E Ki-67. The shape parameters are calculated after segmentation preceded by Otsu thresholding and unsupervised machine learning. Morphological operators help to solve the problem of overlapping of nucleus section in sample images so that the counting will be correct and increase the accuracy of automatic segmentation.Result: With the help of nine morphological features and supported by unsupervised machine learning technique on BreCaHAD dataset, it is predicted the label of breast carcinoma. The performance measures like precision: 95.7%, recall: 93.8%, f-score: 94.74%, accuracy: 0.9088, specificity: 0.6803, BCR: 0.7975 and MCC: 0.5855 are obtained in proposed methodology which is better than existing techniques. Conclusion: This study developed an efficient automated nuclear section segmentation model implemented on BreCaHAD dataset contains H&E stained microscopic biopsy images. Potentially, this model will assist the pathologist for fast, effective, efficient and accurate computation of Ki-67 proliferation score on breast IHC carcinoma images.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

Learning and control

2018 ◽  
Author(s):  
Ivan Herreros
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Brain Function ◽  
Control Strategies ◽  
Learning Problems ◽  
Animal Learning ◽  
Feed Forward Control ◽  
Machine Learning Applications ◽  
And Control

This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement learning problems. Next, it links these concepts with their counterparts in the domain of the psychology of animal learning, highlighting the analogies between supervised learning and classical conditioning, reinforcement learning and operant conditioning, and between unsupervised and perceptual learning. Additionally, it interprets innate and acquired actions from the standpoint of feedback vs anticipatory and adaptive control. Finally, it argues how this framework of translating knowledge between formal and biological disciplines can serve us to not only structure and advance our understanding of brain function but also enrich engineering solutions at the level of robot learning and control with insights coming from biology.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

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