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Proceedings ArticleDOI

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

01 Nov 2018-pp 81-86

TL;DR: 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.

AbstractBreast 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
01 Jan 2020
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.
Abstract: Image segmentation has been an active topic of research for many years. Edges characterize boundaries, and therefore, detection of edges is a problem of fundamental importance in image processing. Edge detection in images significantly reduces the amount of data and filters out useless information while preserving the important structural properties in an image. Edges carry significant information about the image structure and shape, which is useful in various applications related with computer vision. In many applications, the edge detection is used as a pre-processing step. Edge detection is highly beneficial in automated cell counting, structural analysis of the image, automated object detection, shape analysis, optical character recognition, etc. Different filters are developed to find the gradients and detect edges. In this chapter, a new filter (kernel) is proposed, and the compass operator is applied on it to detect edges more efficiently. The results are compared with some of the previously proposed filters both qualitatively and quantitatively.

14 citations

Book ChapterDOI
17 Aug 2019
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.
Abstract: Security of the digital data is one of the major concerns of the today’s world. There are several methods for digital data security that can be found in the literature. Biological sequences have some features that make it worthy for the digital data security processes. 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. This work can be highly beneficial for future research on DNA encryption and can be applied on different domains.

13 citations

Book ChapterDOI
01 Jan 2020
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.
Abstract: Biomedical image analysis methods are gradually shifting towards computer-aided solutions from manual investigations to save time and improve the quality of the diagnosis. Deep learning-assisted biomedical image analysis is one of the major and active research areas. Several researchers are working in this domain because deep learning-assisted computer-aided diagnostic solutions are well known for their efficiency. In this chapter, a comprehensive overview of the deep learning-assisted biomedical image analysis methods is presented. This chapter can be helpful for the researchers to understand the recent developments and drawbacks of the present systems. The discussion is made from the perspective of the computer vision, pattern recognition, and artificial intelligence. This chapter can help to get future research directions to exploit the blessings of deep learning techniques for biomedical image analysis.

13 citations

Book ChapterDOI
17 Aug 2019
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.
Abstract: Biomedical image analysis is an integral part of the modern healthcare industry and has a huge impact on the modern world. Automated computer-aided systems are highly beneficial for fast, accurate and efficient diagnosis of the biomedical images. Remote healthcare systems allow doctors and patients to perform their jobs from separate geographic locations. Moreover, expert opinion about a patient can be obtained from a doctor who is in a different country or in some distant location within stipulated amount of time. Remote healthcare systems require digital biomedical images to be transferred over the network. But several security threats are associated with the transmission of the biomedical images. Privacy of the patients must be preserved by keeping the images safe from any unauthorized access. Moreover, the contents of the biomedical images must be preserved efficiently so that no one can tamper it. Data tampering can produce drastic results in many cases. In this work, a method for biomedical image security has been proposed. DNA encryption method is one of the emerging methods in the field of cryptography. A secure and lossless encryption method is developed in this work. Various numerical parameters are used to evaluate the performance of the proposed method which proves the effectiveness of the algorithm.

10 citations


References
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Journal ArticleDOI
TL;DR: Experiments show that SCG is considerably faster than BP, CGL, and BFGS, and avoids a time consuming line search.
Abstract: A supervised learning algorithm (Scaled Conjugate Gradient, SCG) is introduced. The performance of SCG is benchmarked against that of the standard back propagation algorithm (BP) (Rumelhart, Hinton, & Williams, 1986), the conjugate gradient algorithm with line search (CGL) (Johansson, Dowla, & Goodman, 1990) and the one-step Broyden-Fletcher-Goldfarb-Shanno memoriless quasi-Newton algorithm (BFGS) (Battiti, 1990). SCG is fully-automated, includes no critical user-dependent parameters, and avoids a time consuming line search, which CGL and BFGS use in each iteration in order to determine an appropriate step size. Experiments show that SCG is considerably faster than BP, CGL, and BFGS.

3,587 citations


"Automated Breast Cancer Identificat..." refers methods in this paper

  • ...Back propagation is one of the most popular training method which can be adapted to train ANN [26]....

    [...]

Book
12 Jul 1996
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.
Abstract: We may not be able to make you love reading, but neural networks a systematic introduction will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.

2,233 citations


"Automated Breast Cancer Identificat..." refers methods in this paper

  • ...After finite number of steps the weights are adjusted optimally by using the perceptron rule [25]....

    [...]

Proceedings ArticleDOI
09 Jul 2007
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.
Abstract: Bag-of-features (BoF) deriving from local keypoints has recently appeared promising for object and scene classification. Whether BoF can naturally survive the challenges such as reliability and scalability of visual classification, nevertheless, remains uncertain due to various implementation choices. In this paper, we evaluate various factors which govern the performance of BoF. The factors include the choices of detector, kernel, vocabulary size and weighting scheme. We offer some practical insights in how to optimize the performance by choosing good keypoint detector and kernel. For the weighting scheme, we propose a novel soft-weighting method to assess the significance of a visual word to an image. We experimentally show that the proposed soft-weighting scheme can consistently offer better performance than other popular weighting methods. On both PASCAL-2005 and TRECVID-2006 datasets, our BoF setting generates competitive performance compared to the state-of-the-art techniques. We also show that the BoF is highly complementary to global features. By incorporating the BoF with color and texture features, an improvement of 50% is reported on TRECVID-2006 dataset.

679 citations


"Automated Breast Cancer Identificat..." refers background in this paper

  • ...BAG-OF-FEATURES Bag-of-features model is based on the local feature descriptors that can be collectively used to describe an image [22]....

    [...]

Journal ArticleDOI
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.
Abstract: Today, medical image analysis papers require solid experiments to prove the usefulness of proposed methods. However, experiments are often performed on data selected by the researchers, which may come from different institutions, scanners, and populations. Different evaluation measures may be used, making it difficult to compare the methods. In this paper, we introduce a dataset of 7909 breast cancer histopathology images acquired on 82 patients, which is now publicly available from http://web.inf.ufpr.br/vri/breast-cancer-database . The dataset includes both benign and malignant images. The task associated with this dataset is the automated classification of these images in two classes, which would be a valuable computer-aided diagnosis tool for the clinician. In order to assess the difficulty of this task, we show some preliminary results obtained with state-of-the-art image classification systems. The accuracy ranges from 80% to 85%, showing room for improvement is left. By providing this dataset and a standardized evaluation protocol to the scientific community, we hope to gather researchers in both the medical and the machine learning field to advance toward this clinical application.

526 citations


"Automated Breast Cancer Identificat..." refers methods in this paper

  • ...The proposed method has been tested on the BerkHis Histological dataset [28]....

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Journal ArticleDOI
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.
Abstract: Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.

90 citations


"Automated Breast Cancer Identificat..." refers methods in this paper

  • ...Choice of preprocessing method is dependent on the type of the image [10]....

    [...]