An Improved Mammogram Classification Approach Using Back Propagation Neural Network

Gautam, Aman; Bhateja, Vikrant; Tiwari, Ananya; Satapathy, Suresh Chandra

doi:10.1007/978-981-10-3223-3_35

Cited by 36 publications

(12 citation statements)

References 13 publications

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“…The above discussion noticeably evident that the performance of nature-inspired CSA classifier using haar wavelet outperforms the performance of LDA classifier in classifying the mammogram images. The comparison of classification accuracy of other related existing works (Srivastava et al, 2014;Gautam et al, 2018) with the proposed model is discussed in Table III. As in the table, the comparison is based on the methodology with different feature extraction approaches using various classification algorithms.…”

Section: Proposed Workmentioning

confidence: 99%

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Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

Chakravarthy¹,

Rajaguru²

2019

Asian Pac J Cancer Prev

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Objective:Breast cancer is the most common invasive severity which leads to the second primary cause of death among women. The objective of this paper is to propose a computer-aided approach for the breast cancer classification from the digital mammograms.Methods:Designing an effective classification approach will assist in resolving the difficulties in analyzing digital mammograms. The proposed work utilized the Mammogram Image Analysis Society (MIAS) database for the analysis of breast cancer. Five distinct wavelet families are used for extraction of features from the mammograms of MIAS database. These extracted features are statistical in nature and served as input to the Linear Discriminant Analysis (LDA) and Cuckoo-Search Algorithm (CSA) classifiers. Results:Error rate, Sensitivity, Specificity and Accuracy are the performance measures used and the obtained results clearly state that the CSA used as a classifier affords an accuracy of 97.5% while compared with the LDA classifier. Conclusion:The results of comparative performance analysis show that the CSA classifier outperforms the performance of LDA in terms of breast cancer classification.

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Section: Proposed Workmentioning

confidence: 99%

“…93.88 Pratiwi et al, (2015) Texture features with radial basis function neural network. 93.98 Gautam et al, (2018) Texture features with back propagation neural network. 96.3…”

mentioning

confidence: 99%

Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

Chakravarthy¹,

Rajaguru²

2019

Asian Pac J Cancer Prev

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show abstract

“…The backpropagation algorithm starts with the comparison of output pattern with the target vector [14]. The error values are calculated from the hidden units.…”

Section: E Back-propagation Algorithmmentioning

confidence: 99%

Novel Adaptive Auto-Correction Technique for Enhanced Fingerprint Recognition

P¹,

Srikantaswamy²,

Manjunatha³

2019

IJACSA

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Fingerprints are the most used biometric trait in applications where high level of security is required. Fingerprint image may vary due to various environmental conditions like temperature, humidity, weather etc. Hence, it is necessary to design a fingerprint recognition system that is robust against temperature variations. Existing techniques such as automated and non-automated techniques are not real time analysis (adaptive). In this paper, we propose an adaptive auto correction technique called Reference Auto-correction Algorithm. This proposed algorithm corrects user reference fingerprint template automatically based on captured fingerprint template and the matching score obtained on daily basis to improve the recognition rate. Analysis is carried out on 250 fingerprint templates stored in the database of 10-users captured at varying temperature from 25 0 C to 0 0 C. The experimental result shows 40% improvement in the recognition rate after applying auto correction algorithm.

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“…Level Co-occurrence Matrix (GLCM) texture-based feature is employed for classi ication by (Gautam et al, 2018;Beura et al, 2015;Pratiwi et al, 2015). Cancer is predicted on the basis of change in temperature between the breasts, the thermograms are classi ied into normal and abnormal based on SVM classi ication (Ibrahim et al, 2018).…”

Section: Figure 2: Automated Diagnosis System For Detection Of Breastmentioning

confidence: 99%

“…Breast cancer is a considerable prime cause which affects the lives of women's and leads to death all around the world (Gautam et al, 2018). According to statistical data from the WCR, the proportion of breast tumors in cancers diagnosed is up to 30%, resulting in 15% death due to cancer worldwide.…”

Section: Introductionmentioning

confidence: 99%

Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

N¹,

Rao²

2019

ijrps

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Breast cancer is the second leading cause of death for women everywhere in the world. Since the reason behind the disease remains unknown, early detection and diagnosis is the key challenge for breast cancer control. In this work, mammogram images are initially subject to pre-processing using Laplacian ilter for enhancement of tumour regions, Gaussian mixture model, Gaussian kernel FCM, Otsu global thresholding and FCM technique are employed for segmentation. Further, the ef iciency of segmentation techniques is analyzed by classifying the samples into benign, malignant and healthy using Gray Level Co-occurrence Matrix (GLCM) features. Linear discriminant analysis classiier is used a combination based on which ef iciency used for classi ication of mammograms. Ensemble methods are evaluated. The ef iciency has resulted in better accuracy with the ensemble-based method. The experimentation is conducted in the mini MIAS database of mammograms, and the ef iciency of the linear discriminant analyzer is found to be 89.19% for GKFCM, 83.78% with Otsu and 78.38% with FCM method with GLCM features.

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An Improved Mammogram Classification Approach Using Back Propagation Neural Network

Cited by 36 publications

References 13 publications

Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms

Novel Adaptive Auto-Correction Technique for Enhanced Fingerprint Recognition

Exploration and evaluation of efficient pre-processing and segmentation technique for breast cancer diagnosis based on mammograms

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