scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

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

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.
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.
Citations
More filters
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.

28 citations

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.

24 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.

20 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.

15 citations

Book ChapterDOI
01 Jan 2020
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.
Abstract: Encryption is one of the most frequently used tools in data communications to prevent unwanted access to the data. In the field of image encryption, chaos-based encryption methods have become very popular in the recent years. Chaos-based methods provide a good security mechanism in image communication. In this chapter, chaotic skew-tent map is adapted to encode an image. Seventy-two bit external key is considered (besides the initial parameters of the chaotic system) initially, and after some processing operations, 64 bit internal key is obtained. Using this key, every pixel is processed. The internal key is transformed using some basic operations to enhance the security. The decryption method is very simple so that authentic users can retrieve the information very fast. Every pixel is encrypted using some basic mathematical operations. 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.

11 citations

References
More filters
Journal ArticleDOI
TL;DR: A novel method for cell segmentation and identification has been proposed that incorporated marking cells in cuckoo search (CS) algorithm and experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time.
Abstract: Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity It is also valuable in foremost meadows of technology and medicine Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely Different segments should be identified accurately in order to identify and to count cells in a microscope image Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells Thus, a novel method based on cuckoo search after pre-processing step is employed The method is developed and evaluated on light microscope images of rats' hippocampus which used as a sample for the brain cells The proposed method can be applied on the color images directly The proposed approach incorporates the McCulloch's method for levy flight production in cuckoo search (CS) algorithm Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between-class variance segmentation method and the Tsallis entropy segmentation method Nevertheless, Tsallis entropy method with optimized multi-threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR

94 citations

Book ChapterDOI
01 Jan 2018

50 citations


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

  • ...Computer aided diagnostic systems have a huge impact in the medical industry [7]....

    [...]

Proceedings ArticleDOI
01 Jan 2015
TL;DR: In this article, the authors presented an algorithm based on simulated annealing method to solve the job shop scheduling problem, which is an approximation algorithm for finding the minimum makespan in a job shop.
Abstract: The Job Shop Scheduling Problem (known as JSSP) is a wellknown and one of the difficult combinatorial optimization problems and treated as a member of NP-complete problem class. This paper presents an algorithm based on Simulated Annealing method to solve the Job Shop Scheduling problem. It is an approximation algorithm for finding the minimum makespan in a job shop. The proposed algorithm is based on Roulette wheel selection and simulated annealing, a generalization of the well known and effective iterative improvement approach for combinatorial optimization problems. The generalization involves the acceptance of cost-increasing transitions with a nonzero probability to avoid getting stuck in local minima. The problem studied in this research focuses on the sequencing of operations and allocation of operation to the machine under some sequence constraint.

39 citations


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

  • ...Some metaheuristic algorithms [12]–[14] have been used to update the weights [15]....

    [...]

Proceedings ArticleDOI
01 Aug 2017
TL;DR: Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features, which has been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure.
Abstract: Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma, basal cell carcinoma and lentigo simplex. Images have been obtained from International Skin Imaging Collaboration (ISIC) dataset. A popular multi objective optimization method called Non-dominated Sorting Genetic Algorithm — II is employed to train the ANN (NNNSGA-II). Different feature have been extracted to train the classifier. A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm). The results have been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure. Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features.

39 citations


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

  • ...Some metaheuristic algorithms [12]–[14] have been used to update the weights [15]....

    [...]

Journal ArticleDOI
TL;DR: An algorithm based on integrating Genetic Algorithms and Simulated Annealing methods to solve the Job Shop Scheduling problem and is an approximation algorithm for the optimization problem i.e. obtaining the minimum makespan in a job shop.
Abstract: The Job-Shop Scheduling Problem (JSSP) is a well-known and one of the challenging combinatorial optimization problems and falls in the NP-complete problem class. This paper presents an algorithm based on integrating Genetic Algorithms and Simulated Annealing methods to solve the Job Shop Scheduling problem. The procedure is an approximation algorithm for the optimization problem i.e. obtaining the minimum makespan in a job shop. The proposed algorithm is based on Genetic algorithm and simulated annealing. SA is an iterative well known improvement to combinatorial optimization problems. The procedure considers the acceptance of cost-increasing solutions with a nonzero probability to overcome the local minima. The problem studied in this research paper moves around the allocation of different operation to the machine and sequencing of those operations under some specific sequence constraint.

39 citations


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

  • ...Some metaheuristic algorithms [12]–[14] have been used to update the weights [15]....

    [...]