Proposal for an incremental learning method for CNN-based breast cancer tumor detection CAD development

2020 ◽  
Author(s):  
Kazuya Abe ◽  
Hideya Takeo ◽  
Yuuichi Nagai ◽  
Shigeru Nawano
Keyword(s):  
Breast Cancer ◽  
Incremental Learning ◽  
Tumor Detection ◽  
Learning Method ◽  
Cancer Tumor ◽  
Incremental Learning Method

A robust incremental learning method for non-stationary environments

2011 ◽  
Vol 74 (11) ◽  
pp. 1800-1808 ◽  
Author(s):  
David Martínez-Rego ◽  
Beatriz Pérez-Sánchez ◽  
Oscar Fontenla-Romero ◽  
Amparo Alonso-Betanzos
Keyword(s):  
Incremental Learning ◽  
Learning Method ◽  
Incremental Learning Method

scholarly journals Early Breast Cancer Tumor Detection on Mammogram Images

2014 ◽  
Vol 87 (14) ◽  
pp. 14-18 ◽  
Author(s):  
Naishil N.Shah ◽  
Tushar V. Ratanpara ◽  
C. K. Bhensdadia
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Tumor Detection ◽  
Cancer Tumor ◽  
Mammogram Images

Incremental Learning Method of Simple-PCA

2008 ◽  
pp. 403-410 ◽  
Author(s):  
Tadahiro Oyama ◽  
Stephen Karungaru ◽  
Satoru Tsuge ◽  
Yasue Mitsukura ◽  
Minoru Fukumi
Keyword(s):  
Incremental Learning ◽  
Learning Method ◽  
Incremental Learning Method

Incremental Regressive Learning Algorithm of Support Vector Machine and its Application

2011 ◽  
Vol 216 ◽  
pp. 301-306
Author(s):  
Shi Hua Zhang ◽  
Xi Long Qu ◽  
Xue Ye Wang
Keyword(s):  
Support Vector Machine ◽  
Transition Temperature ◽  
Incremental Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Learning Ability ◽  
Support Vector ◽  
Learning Method ◽  
Essential Characteristic ◽  
Incremental Learning Method

There is no incremental learning ability for the traditional support vector machine (SVM) and there are all kind of merits and flaws for usually used incremental learning method. Normal SVM is unable to train in large-scale samples, while the computer’s memory is limited. In order to resolve this problem and improve training speed of the SVM, we analyze essential characteristic of SVM and bring up the incremental learning algorithm of SVM based on regression of SVM related to SV (support vectors). The algorithm increases the speed of training and can be able to learning with large-scale samples while its regressive precision loses fewer. The experiments show that SVM performs effectively and practically. Its application to prediction of the transition temperature (Tg) for high molecular polymers show that this model (R2=0.9427) proved to be considerably more accurate compared to a ANNs regression model (R2=0.9269).

Incremental-Learning Spam Messages Distinguishing and Sorting System Based on Arm Platform

2014 ◽  
Vol 602-605 ◽  
pp. 3843-3845
Author(s):  
Xue Bing She
Keyword(s):  
Mobile Phone ◽  
Mobile Phones ◽  
Incremental Learning ◽  
Learning Method ◽  
Normal Functions ◽  
General Performance ◽  
Self Study ◽  
Sorting System ◽  
Incremental Learning Method ◽  
Test Result

Now that the endless spam messages have sevely affected people on their works and daliy lives. The omission and even the help of the serrvice providrs (SP) in this regard make it extremely necessary to distinguish and sort the spam messages through filtering the messages on user’s mobile phone. The paper expatiated on a “self-study spam messages distinguishing and soring system’ developed onARM9 platform. By adding a spam box to SMS software of cellphome besidrs all os its normal functions, the system incessantly adjusts the weight of the featues though incremental learning method so as to achieve the highly accurate discrimination on whether a message received is the spam one or not, and futher decides to sort the message to the message box or the spam box, Test result on ARM9 platform shows, our technology can be applied completely to the mobile phones with just general performance.

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