IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Purpose-Pre-screening of skin lesion for malignancy is highly demanded as melanoma being a life-threatening skin cancer due to unpaired DNA damage. In this paper, lesion segmentation based on Fuzzy C-Means clustering using non-dermoscopic images has been proposed. Design/methodology/approach-The proposed methodology consists of automatic cluster selection for FCM using the histogram property. The system used the local maxima along with Euclidean distance to detect the binomial distribution property of the image histogram, to segment the melanoma from normal skin. As the Value channel of HSV color image provides better and distinct histogram distribution based on the entropy, it has been used for segmentation purpose. Findings-The proposed system can effectively segment the lesion region from the normal skin. The system provides a segmentation accuracy of 95.69 % and the comparative analysis has been performed with various segmentation methods. From the analysis, it has been observed that the proposed system can effectively segment the lesion region from normal skin automatically. Originality/Value-This paper suggests a new approach for skin lesion segmentation based on FCM with automatic cluster selection. Here, different color channel has also been analyzed using entropy to select the better channel for segmentation. In future, the classification of melanoma from benign naevi can be performed.

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images

Devi¹,

Singh²,

Laskar³

2020

“…Dubey, et al in [12] have proposed K-Means clustering segmentation technique to detect infected fruit part. Belkhodja and Hamdadou have also proposed a computer aided detection system for detecting breast masses in [13]. Pujari, Yakkundimath and Byadgi have used SVM and ANN for classification of plant disease in [14].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Technique for Grape Fanleaf Virus Detection

Mohammadpoor¹,

Nooghabi²,

Ahmedi³

2020

Grapevine Fanleaf Virus (GFLV) is one of the most important viral diseases of grapes, which can damage up to 85% of the crop, if not treated at the right time. The aim of this study is to identify infected leaves with GFLV using artificial intelligent methods using an accessible database. To do this, some pictures are taken from infected and healthy leaves of grapes and labeled by technical specialists using conventional laboratory methods. In order to provide an intelligent method for distinguishing infected leaves from healthy ones, the area of unhealthy parts of each leaf is highlighted using Fuzzy C-mean Algorithm (FCM), and then the percentages of the first two segments area are fed to a Support Vector Machines (SVM). To increase the diagnostic reliability of the system, K-fold cross validation method with k = 3 and k =5 is applied. After applying the proposed method over all images using K-fold validation technique, average confusion matrix is extracted to show the True Positive, True Negative, False Positive and False Negative percentages of classification. The results show that specificity, as the ability of the algorithm to really detect healthy images, is 100%, and sensitivity, as the ability of the algorithm to correctly detect infected images is around 97.3%. The average accuracy of the system is around 98.6%. The results imply the ability of the proposed method compared to previous methods.

“…In literature, there is an enormous amount of machine learning applications where feature selection has been applied. Some of these applications involve medical diagnosis [4], facial expression recognition [5], diagnose of bronchitis [6], gene selection and cancer classification [7], image steganalysis [8], big data classification [9], obstructive sleep apnea diagnosis [10], sentiment classification [11], Mobile Agent Platform Protection [70], Irony Detection [71], categorize text documents [72], classification of Plant Diseases [73], Breast Masses Detection [74].…”

Section: Introduction and Rationalementioning

confidence: 99%

Binary Multi-Verse Optimization (BMVO) Approaches for Feature Selection

Hans¹,

Kaur²

2020

Multi-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process.