iosr-JVSP   Volume-10 ~ Issue-6 ~ Nov. - Dec. 2020

ABSTRACT: Breast cancer is the most commonly diagnosed life threatening cancer in women worldwide. Breast cancer is the leading cause of cancer death among women. Early detection is of great significance and essential to the treatment of breast cancer. Ultrasonography is one of the most widespread imaging modality used to detect and classify abnormalities of the breast. This paper proposes the use of wavelet transform and its coefficients as texture features for the detection of abnormalities in the breast. Gray level co-occurrence matrix is computed from wavelet coefficients at two levels. Principal component analysis and genetic algorithms are used for feature reduction and selection. Support vector machine (SVM) and Naïve Bayes (NB) are used to differentiate benign and malignant lesions. Their performances are evaluated using diagnostic accuracy, sensitivity, specificity, positive.....

Keywords: Breast lesion, Genetic algorithm, Principal component analysis, Ultrasonography, Wavelet transform..

[1] R. L. Siegel, K. D. Miller, A. Jemal, Cancer statistics, 2017, Cancer J. Clin., 67(1), 2017, 7–30 .
[2] Malvia, S., Bagadi, S. A., Dubey, U. S. and Saxena, S, Epidemiology of Breast Cancer in Indian Women, Asia-Pac J Clin Oncol, 13(4), 2017, 289–295.
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ABSTRACT: Optical Character Recognition (OCR) is a technology that extracts all the text from the images, .pdf documents or scanned files. So OCR converts normal scanned documents text-searchable so to allow content search on the same. Hindi being the national language of India, with such huge population makes document managing and preservation difficult in government sector. Hence, this paper presents an efficient algorithm Fuzzy KNN for recognition of Hindi script characters from printed documents. Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate due to shirorekha as well as joint characters. This paper proposes an OCR for printed Hindi text in Devanagari script, using Fuzzy KNN which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation also. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem, when designing an effective character segmentation technique. Here, Fuzzy KNN classifier in pair with two different features Geometric and Wavelet features are used to handle this problem...

Keywords: Optical Character Recognition, Fuzz-KNN, Wavelet Transform

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[4]. "Optical Character Recognition" by Jesse Hansen.
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ABSTRACT: This paper proposes efficient constant multiplier architecture based on vertical-horizontal binary common sub-expression elimination (VHBCSE) algorithm based on carry select adder using common Boolean logic and modified full adder for planning a reconfigurable finite impulse response(FIR) channel whose coefficients can progressively change continuously. To plan a proficient reconfigurable FIR channel, as indicated by the proposed modified VHBCSE calculation, 2-bit paired regular sub-articulation end (BCSE) calculation has been connected vertically crosswise over contiguous coefficients on the 2-D space of the coefficient lattice at first, taken after by applying variable-piece BCSE calculation evenly inside every coefficient. This method is fit for lessening......

Keywords: Index Terms—VHBCSE algorithm, MCM, FIR filter, VLSI design

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