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Journal ArticleDOI

Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis-An SVM based approach

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TLDR
An automated classification method is presented for understanding the deviation of normal structural profile of oral mucosa during precancerous changes and unveils the opportunity to understand OSF related changes in cell population having definite geometric properties.
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This article is published in Computers in Biology and Medicine.The article was published on 2009-12-01. It has received 48 citations till now. The article focuses on the topics: Population.

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Oral Submucous Fibrosis: A Review on Etiopathogenesis, Diagnosis, and Therapy

TL;DR: Oral submucous fibrosis is introduced from a molecular perspective and what is known about its underlying mechanisms, diagnostic biomarkers, and therapeutic interventions are summarized.
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Histopathological image analysis using image processing techniques : an overview

TL;DR: This paper reviews and summarizes the applications of digital image processing techniques for histology image analysis mainly to cover segmentation and disease classification methods.
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An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier

TL;DR: This paper proposes an optimal diagnosis system not only for early detection of lung cancer nodules and also to improve the accuracy in Fog computing environment to achieve high privacy, low latency and mobility support.
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Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review.

TL;DR: Early evidence is provided to support the potential application of supervised machine learning methods as a diagnostic aid for some oral potentially malignant and malignant lesions; however, there is a paucity of evidence using AI for diagnosis of other head and neck pathologies.
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Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis

TL;DR: A two-stage approach is proposed for computing oral histology images, where 12-layered (7 × 7×3 channel patches) deep convolution neural network (CNN) are used for segmentation of constituent layers in the first stage and in the second stage the keratin pearls are detected from the segmented keratin regions using texture-based feature (Gabor filter) trained random forests.
References
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Statistical learning theory

TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
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Statistical Pattern Recognition

Alex M. Andrew
- 01 Apr 2000 - 
TL;DR: Introduction to statistical pattern recognition and nonlinear discriminant analysis - statistical methods.
Book

Statistical Pattern Recognition

TL;DR: In this paper, the authors propose a statistical pattern recognition method for pattern recognition using neural networks and nonlinear discriminant analysis (NDA) based on classification trees and feature selection and extraction.
Journal ArticleDOI

Image Processing - Principles and Applications

TL;DR: This PDF file contains the editorial “Image Processing: Principles and Applications” for JEI Vol.
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Support vector machines combined with feature selection for breast cancer diagnosis

TL;DR: The results show that the highest classification accuracy (99.51%) is obtained for the SVM model that contains five features, and this is very promising compared to the previously reported results.
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