Mammogram mass classification using various geometric shape and margin features for early detection of breast cancer

Surendiran, B.; Vadivel, A.

doi:10.1504/ijmei.2012.045302

Cited by 32 publications

(19 citation statements)

References 21 publications

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“…The sentence datasets were collected on both the DDSM dataset and the FFDM dataset. Before composing sentences, we investigated words and phrases for describing BI-RADS mass lexicons (margin and shape) in the medical papers [17,18,19,20,21,22,23,24] and its synonyms called visual words. Visual words of each lexicon included 5-12 words or phrases.…”

Section: Experimental Conditionmentioning

confidence: 99%

Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis

Lee

Kim

2019

Lecture Notes in Computer Science

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The ambiguity of the decision-making process has been pointed out as the main obstacle to applying the deep learningbased method in a practical way in spite of its outstanding performance. Interpretability could guarantee the confidence of deep learning system, therefore it is particularly important in the medical field. In this study, a novel deep network is proposed to explain the diagnostic decision with visual pointing map and diagnostic sentence justifying result simultaneously. For the purpose of increasing the accuracy of sentence generation, a visual word constraint model is devised in training justification generator. To verify the proposed method, comparative experiments were conducted on the problem of the diagnosis of breast masses. Experimental results demonstrated that the proposed deep network could explain diagnosis more accurately with various textual justifications.

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Section: Experimental Conditionmentioning

confidence: 99%

Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis

Lee

Kim

2019

Lecture Notes in Computer Science

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“…Asad et al (2011), with only 7 attributes extracted from geometric shape of the ROIs, reached the rate of 80% of accuracy [Asad et al 2011]. Surendiran and Vadivel (2012) used a large number of images for testing and 17 attributes of shape and edge, to reach 93.72% of accuracy [Surendiran and Vadivel 2012]. More recently, Liu and Tang (2014) achieved 94% of accuracy, extracting 12 attributes of shape and texture from 826 images [Liu and Tang 2014].…”

Section: Methodsmentioning

confidence: 99%

“…In another approach, mammograms were classified using geometric attributes and features extracted from the edge of regions for the detection of breast cancer [Surendiran and Vadivel 2012]. From 940 images of DDSM database, 17 attributes were extracted.…”

Section: Computer-aided Diagnosis: Related Workmentioning

confidence: 99%

Classification of Breast Tumors Through Image Mining Techniques

Souto¹,

Santos²,

Silva³

2018

Anais Do Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS)

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In this paper a methodology and a computer-aided diagnosis system for detection of breast cancer are proposed. The approach involves Image Processing resources to extract morphological features from tumors in mammograms and Image Mining to classify them as benign or malignant. Images from BCDR repository were used for the experiments. The results showed the efficacy of the proposed method and system, which reduced the false positive and false negative rates, and allowed a more efficient decision-making process.

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“…Various studies have been performed on shape characteristics of mammograms and they are able to classify masses effectively (Ertas et al, 2001;Sun et al, 2002;Shah et al, 2004;Flores and Gonzalez, 2004;Retico et al, 2006;Surendiran and Vadivel, 2012). Various DDSM database descriptors are used for classifying mammogram masses (Markey et al, 2002;Gupta et al, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

Effect of BIRADS shape descriptors on breast cancer analysis

Surendiran¹,

Ramanathan²,

Vadivel

2015

IJMEI

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This paper focuses on an approach for characterising the spatial structure of mammogram masses using various multimodal features. Experiments have been conducted on benchmark digital database for screening mammography (DDSM) database with 300 mammograms. According to Breast Imaging Reporting and Data System (BIRADS) spatial structure of mammogram masses can be discriminated using its shape, size and density properties. Various new geometrical shape, margin and texture features and DDSM descriptors are used to characterise the morphology of masses as either benign or malignant. The effect of DDSM descriptors on classification accuracy is analysed. The experimental results shows that all 20 features including DDSM descriptors produce better accuracy of 93.3% compared to 86.7% without using DDSM descriptors. The overall Wilk's lambda value is reduced when all 20 features are used for classification. The proposed features set using shape, texture and DDSM descriptors provide better (c/v) ratio compared to wavelet, Gabor and other features reported in the literature.Reference to this paper should be made as follows: Surendiran, B., Ramanathan, P. and Vadivel, A. (2015) 'Effect of BIRADS shape descriptors on breast cancer analysis', Int.

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Mammogram mass classification using various geometric shape and margin features for early detection of breast cancer

Cited by 32 publications

References 21 publications

Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis

Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis

Classification of Breast Tumors Through Image Mining Techniques

Effect of BIRADS shape descriptors on breast cancer analysis

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