Medical Image Retrieval based on LBP Histogram Fourier features and KNN classifier

Bharathi, P.; Reddy, K.R.; Srilakshmi, G.

doi:10.1109/icaetr.2014.7012840

Cited by 6 publications

(2 citation statements)

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“…In [9], authors have employed SVM classifier in their work and have achieved an accuracy of 92.5 %. Use of Knearest Neighbor (KNN) Classifier for medical image retrieval is discussed in [10]. Feature extraction from the identified liver tumors is done using Average Correction Higher order Local Autocorrelation Coefficient (ACHLAC) and Legendre moments.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Analysis of Classification of Liver Tumors using Support Vector Machine and Rough Set based Classifiers

2019

IJITEE

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In recent years the medical diagnosis is majorly done based on the medical images captured using various imaging modalities. The medical doctors and radiologists use these medical images to identify the pathological problems or diseases and suggest the patient about further treatment. In this process, medical doctors and radiologists often prefer to make use of software which can assist in taking the decision. Such an approach is called as Computer Aided Diagnosis (CAD). The CAD systems usually comprise of many phases like segmentation of portion corresponding to a particular organ or region under consideration, detecting the pathology bearing area in that and further classifying into various disease classes. Here, Accuracy of classifiers majorly decides the effectiveness of the diagnosis. In this paper, classification of liver tumors into benign and malignant is considered as a case study. Implementation of two different classifiers namely Support Vector Machine and Rough Set based classifier is carried out using set of features extracted such as Texture features using Average Correction Higher order Local Autocorrelation Coefficients and shape features using Legendre moments. Comparison of performance of both the classifiers is made and tabulated. Here, Rough Set based classifier has performed better when compared with Support Vector Machine.

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Section: Literature Surveymentioning

confidence: 99%

Performance Analysis of Classification of Liver Tumors using Support Vector Machine and Rough Set based Classifiers

2019

IJITEE

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“…K-nearest neighbor (k-NN), support vector machine (SVM) and machine learning (ML) is an example technique popular to solve image recognition problem. The simplest one of the three is k-NN, which do the classification by searching the most similar image from the dataset [6][7][8][9][10][11]. SVM basically try to projecting input data to feature space.…”

Section: Introductionmentioning

confidence: 99%

Expert role in image classification using CNN for hard to identify object: distinguishing batik and its imitation

Widyantoko¹,

Widowati²,

Isnaini³

et al. 2021

IJ-AI

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<span id="docs-internal-guid-25a2977b-7fff-96bd-b93a-19bd55e68ea7"><span>In this research we try to solve the recognition problem in differentiating between batik and its imitation. Batik is an Indonesian heritage of process in making traditional textile product that is now endangered by the existence of imitation products. We try to compare two popular CNN model to classify batik products into five classes. The classes are tulis, cap, print warna, print malam, cabut warna. Tulis and cap are genuine batik, and the other three are an imitation. We realize that this problem is go beyond the recognition of fine grained image problem, it is a hard to identify image problem because even the batik experts is having a hard time identifying batik and its imitation if only based on its picture. The two CNN models, inceptionV3 and mobilenetV2 were trained on three types of image. One type is a freely taken image, the other two were taken based on the experts suggestion. The accuracy score shows that the model trained with the suggestion based picture perform better than the one trained with the random picture.</span></span>

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Medical Image Retrieval Using Efficient Texture and Color Patterns with Neural Network Classifier

Kumar

Sathiamoorthy

2020

Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019)

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Medical Image Retrieval based on LBP Histogram Fourier features and KNN classifier

Cited by 6 publications

References 5 publications

Performance Analysis of Classification of Liver Tumors using Support Vector Machine and Rough Set based Classifiers

Performance Analysis of Classification of Liver Tumors using Support Vector Machine and Rough Set based Classifiers

Expert role in image classification using CNN for hard to identify object: distinguishing batik and its imitation

Medical Image Retrieval Using Efficient Texture and Color Patterns with Neural Network Classifier

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