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Proceedings ArticleDOI

Automated Breast Cancer Identification by analyzing Histology Slides using Metaheuristic Supported Supervised Classification coupled with Bag-of-Features

TLDR
An automated computer assisted framework has been proposed to analyze and detect the type of the disease from the current condition of the breast and three models have been compared in terms of accuracy.
Abstract
Breast cancer is one of the major threats to the human being. Early identification can prevent some of the premature deaths. Manual methods are sometimes very tedious and time consuming. Moreover manual diagnosis can be prone to error. Automated analysis can reduce the overhead of the manual diagnosis and reduce the error. In this work, an automated computer assisted framework has been proposed to analyze and detect the type of the disease from the current condition of the breast. Histological slides have been used for automated diagnosis. SIFT based feature selection and extraction method has been used followed by a Bag-of-Features method. The extracted features are classified by a metaheuristic supported Artificial Neural Network. Three models have been compared in terms of accuracy and obtained results are reported in a comprehensive manner.

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Citations
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Book ChapterDOI

An Overview of Biomedical Image Analysis From the Deep Learning Perspective

TL;DR: In this chapter, a comprehensive overview of the deep learning-assisted biomedical image analysis methods is presented and can be helpful for the researchers to understand the recent developments and drawbacks of the present systems.
Book ChapterDOI

An Advanced Approach to Detect Edges of Digital Images for Image Segmentation

TL;DR: This chapter proposes a new filter (kernel), and the compass operator is applied on it to detect edges more efficiently, and the results are compared with some of the previously proposed filters both qualitatively and quantitatively.
Book ChapterDOI

Data Security Techniques Based on DNA Encryption

TL;DR: In this work, DNA encryption and its different approaches are discussed to give a brief overview on the data security methods based on DNA encryption.
Book ChapterDOI

Biomedical Image Security Using Matrix Manipulation and DNA Encryption

TL;DR: A secure and lossless encryption method is developed in this work and various numerical parameters are used to evaluate the performance of the proposed method which proves the effectiveness of the algorithm.
Book ChapterDOI

A Robust Image Encryption Method Using Chaotic Skew-Tent Map

TL;DR: In this chapter, chaotic skew-tent map is adapted to encode an image and the values of various test parameters show the power and efficiency of the proposed algorithm, which can be used as a safeguard for sensitive image data and a secure method of image transmission.
References
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Journal ArticleDOI

Original Contribution: A scaled conjugate gradient algorithm for fast supervised learning

TL;DR: Experiments show that SCG is considerably faster than BP, CGL, and BFGS, and avoids a time consuming line search.
Book

Neural Networks: A Systematic Introduction

Raúl Rojas
TL;DR: The authors may not be able to make you love reading, but neural networks a systematic introduction will lead you to love reading starting from now.
Journal ArticleDOI

A Dataset for Breast Cancer Histopathological Image Classification

TL;DR: A dataset of 7909 breast cancer histopathology images acquired on 82 patients, which is now publicly available from http://web.ufpr.br/vri/breast-cancer-database, aimed at automated classification of these images in two classes, which would be a valuable computer-aided diagnosis tool for the clinician.
Proceedings ArticleDOI

Towards optimal bag-of-features for object categorization and semantic video retrieval

TL;DR: This paper evaluates various factors which govern the performance of Bag-of-features, and proposes a novel soft-weighting method to assess the significance of a visual word to an image and experimentally shows it can consistently offer better performance than other popular weighting methods.
Journal ArticleDOI

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

TL;DR: A novel real time integrated method to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score.
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