Independent Component Analysis for Vision-inspired Classification of Retinal Images with Age-related Macular Degeneration

2008 ◽  
Author(s):  
P. Soliz ◽  
S.R. Russell ◽  
M.D. Abramoff ◽  
S. Murillo ◽  
M. Pattichis ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Macular Degeneration ◽  
Component Analysis ◽  
Independent Component ◽  
Age Related Macular Degeneration ◽  
Retinal Images ◽  
Age Related

scholarly journals Deep Learning‐based Classification of Resting‐state fMRI Independent‐component Analysis

2021 ◽  
Author(s):  
Victor Nozais ◽  
Philippe Boutinaud ◽  
Violaine Verrecchia ◽  
Marie-Fateye Gueye ◽  
Pierre-Yves Hervé ◽  
...  
Keyword(s):  
Deep Learning ◽  
Independent Component Analysis ◽  
Resting State ◽  
Resting State Fmri ◽  
Component Analysis ◽  
Independent Component

scholarly journals Improving Efficiency in Separating Blood Vessels from Retinal Images with Deep Learning Techniques

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3637-3640
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Macular Degeneration ◽  
Blood Vessels ◽  
Age Related Macular Degeneration ◽  
Retinal Images ◽  
Retinal Vessels ◽  
Age Related ◽  
Learning Techniques ◽  
Retinal Pathologies

Retinal vessels ID means to isolate the distinctive retinal configuration issues, either wide or restricted from fundus picture foundation, for example, optic circle, macula, and unusual sores. Retinal vessels recognizable proof investigations are drawing in increasingly more consideration today because of pivotal data contained in structure which is helpful for the identification and analysis of an assortment of retinal pathologies included yet not restricted to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the advancement of right around two decades, the inventive methodologies applying PC supported systems for portioning retinal vessels winding up increasingly significant and coming nearer. Various kinds of retinal vessels segmentation strategies discussed by using Deep Learning methods. At that point, the pre-processing activities and the best in class strategies for retinal vessels distinguishing proof are presented.

Analysis of Microcalcifications Using Block Wise Local Binary Pattern and Independent component analysis in Mammograms

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Spandana Paramkusham ◽  
Dr. Kunda M.M. Rao ◽  
Dr. BVVSN Prabhakar Rao
Keyword(s):  
Breast Cancer ◽  
Independent Component Analysis ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Component Analysis ◽  
Independent Component ◽  
Significant Shift ◽  
Statistical Features ◽  
Calcium Deposits

In India, the average age of developing a breast cancer has undergone a significant shift over last few decades. Most prominent features that indicate breast cancer are microcalcifications. Microcalcifications are tiny calcium deposits deposited on skin and non-palpable. Automatic analysis of microcalcification helps specialist in having more precise decision. The paper presents an approach that involves classification of microcalcifications into benign/malignant in mammograms. Texture features such LBP and statistical features are extracted from ROIs with microcalcification and independent component analysis is applied to reduce the feature set. These feature set is fed to artificial neural networks to classify the ROIs into malignant and benign calcifications.

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