Mammogram classification using Extreme Learning Machine and Genetic Programming

Menaka, K.; Karpagavalli, S.

doi:10.1109/iccci.2014.6921724

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…Figure 5 shows the signature of banana fruit. [12]. A comparison of banana fruit detection using Binary SVM is obtained with different kernel function and its accuracy rate is tabulated in Tablel.…”

Section: Resultsmentioning

confidence: 99%

Shape based approach for detecting Musa Species in fruit industry

Senthilarasi¹,

Roomi²,

Prasanna³

2014

2014 Sixth International Conference on Advanced Computing (ICoAC)

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Agro export industries generate a substantial amount of revenue to Indian economy. In the fruit industry, various fruits like banana, mango, apple and pomegranate, etc. are transported in the conveyor for a post harvest process like classification, sorting, grading and juice extraction. The manual discrimination of various fruits consumes time and, it can be automated. This research work is intended to build an image processing algorithm that ensures automatic discrimination of banana (Musa Species.) from other fruits like Citrus, Apple, and Pomegranate. The input object is segmented using Background subtraction and threshold method. Morphological operations are performed to obtain the clear contour of the segmented objects. The shape of the banana and non-banana are described by scale and translation invariant signature. Binary SVM with signature feature vectors detect the banana fruit from the non-banana fruit automatically. The accuracy rate is 95%.

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Section: Resultsmentioning

confidence: 99%

Shape based approach for detecting Musa Species in fruit industry

Senthilarasi¹,

Roomi²,

Prasanna³

2014

2014 Sixth International Conference on Advanced Computing (ICoAC)

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“…Menaka et al used SVM with genetic algorithm on breast cancer dataset and achieved a classification accuracy of 99.78% on begging and 97.67% on Malignant [13]. Hussain et al proposed a model with genetic algorithm and fuzzy logic with breast cancer data with accuracy of 97.95% [14]. Chandra P et al .proposed a method using Artificial Neural Network and extreme learning technique on breast cancer data and achieved accuracy of 98% [15].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Comparative Study for Diagnosis of Breast Cancer Datasets

Sahu¹,

Dash²,

Mohanty³

et al. 2018

IJET

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Every disease is curable if a little amount of human effort is applied for early diagnosis. The death rate in world increases day by day as patient fail to detect it before it becomes chronic. Breast cancer is curable if detection is done at early stage before it spread across all part of body. Now-a-days computer aided diagnosis are automated assistance for the doctors to produce accurate prediction about the stage of disease. This study provided CAD system for diagnosis of breast cancer. This method uses Neural Network (NN) as a classifier model and PCA/LDA for dimension reduction method to attain higher classification rate. Multiple layers of neural network are applied to classify the breast cancer data. This system experiment done on Wisconsin breast cancer dataset (WBCD) from UCI repository. The dataset is divided into 2 parts train and test. With the result of accuracy, sensitivity, specificity, precision and recall the performance can be measured. The results obtained are this study is 97% using ANN and PCA-ANN, which is better than other state-of-art methods. As per the result analysis this system outperformed then the existing system.

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“…In 2014, Seema Singh, Sushmitha H, Harini J and Surabhi B.R, proposed an automated technique using a multilayer Feed Forward network using Back Propagation algorithm is used to classify the WDBC Dataset. In this paper the author used scaled conjugate gradient (SCG) and Bayesian regularization (BR) to predict the accuracy and result with SCG for WBCD was 97.51% of accuracy [21].…”

Section: In 2012 Yasmeen M George Bassant Mohamed Elbagoury Hala mentioning

confidence: 99%