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Proceedings ArticleDOI

Efficient Gabor Filter Using Vedic Mathematic for High Speed Convolution in Skin Cancer Detection

TLDR
An improved design of Gabon filter is proposed which is combined with Vedic algorithm (Urdhva Triyagbhyam) to give faster convolution result to detect early stages of skin cancer using textural properties of skin.
Abstract
Normal moles are mostly small brown in color that are spots or growths on the skin that is by birth or emerge in the first few decades of life in almost everyone. Skin cancer most often appears as moles. Seldom people are aware about the skin cancer. If detected at early stages then it can be cured. We propose an improved design of Gabon filter which is combined with Vedic algorithm (Urdhva Triyagbhyam) to give faster convolution result. The exertion has proved the efficiency of Urdhva Triyagbhyam which is the Vedic method of multiplication that enables parallel generation of intermediary products that eliminates unwanted multiplication steps with zeros and scaled to higher bit. In this paper we employ the filter design appropriate for detecting the early stages of skin cancer using textural properties of skin.

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Citations
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Proceedings ArticleDOI

Design and simulation of enhanced 64-bit Vedic multiplier

TL;DR: Comparative analysis demonstrates that the proposed architecture for multiplication produce better results even for higher bits in terms of speed.
Journal ArticleDOI

Applications of Vedic multiplier - A Review

TL;DR: This paper is a review of the application and modification of Vedic multiplier in different fields and a comparison of VedIC multiplier with other multipliers for enhancing performance parameters.
Journal ArticleDOI

A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images

TL;DR: In this paper , a fully connected neural network (FCNN) was used for semantic segmentation and a stacked Restricted Boltzmann Machine (RBM) classifies skin ROIs.
Journal ArticleDOI

An Effective Automatic Breast Cancer Identification using Vedic Mathematics

TL;DR: A method is proposed here to identify the breast cancer using image processing techniques which is blended with Vedic Mathematics to assist the pathologists to improve the accuracy, efficiency in detecting grade of cancer and to minimize the inter-observer variation.
References
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Journal ArticleDOI

Minimization of Region-Scalable Fitting Energy for Image Segmentation

TL;DR: This work proposes a region-based active contour model that draws upon intensity information in local regions at a controllable scale to cope with intensity inhomogeneity and shows desirable performances of this model.
Journal ArticleDOI

Estimating the probability of the presence of a signal of interest in multiresolution single- and multiband image denoising

TL;DR: The results demonstrate that the new subband-adaptive shrinkage function outperforms Bayesian thresholding approaches in terms of mean-squared error and the spatially adaptive version of the proposed method yields better results than the existing spatiallyadaptive ones of similar and higher complexity.
Journal ArticleDOI

Designing Gabor filters for optimal texture separability

TL;DR: Overall, using the Gabor "lter magnitude response given a frequency bandwidth and spacing of one octave and orientation bandwidth and spaced of 303 augmented by a measure of the texture complexity generated preferred results.
Journal ArticleDOI

Skin cancer identification using multifrequency electrical impedance-a potential screening tool

TL;DR: It was found that the power of skin cancer detection using electrical impedance is as good as, or better than, conventional visual screening made by general practitioners.
Proceedings ArticleDOI

Invariant texture segmentation via circular Gabor filters

TL;DR: A new method using circular Gabor filters (CGF) for rotation invariant texture segmentation is proposed, modified into a circular symmetric version.
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