Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms

Sapate, Suhas G.; Talbar, Sanjay N.; Mahajan, Abhishek; Sable, Nilesh; Desai, Subhash; Thakur, Meenakshi

doi:10.1016/j.bbe.2019.04.008

Cited by 24 publications

(9 citation statements)

References 34 publications

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“…Several imaging modalities are applied for breast cancer detection like X-ray, ultrasound, magnetic resonance imaging (MRI), histology, positron emission tomography (PET), and computerized tomography (CT). [ 3 ]. The mammogram image is the best choice among other imaging modalities for breast cancer diagnosis, because of its high reliability and cost-effectiveness.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

Ittannavar¹,

Havaldar

2022

BioMed Research International

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In recent times, breast mass is the most diagnostic sign for early detection of breast cancer, where the precise segmentation of masses is important to reduce the mortality rate. This research proposes a new multiobjective optimization technique for segmenting the breast masses from the mammographic image. The proposed model includes three phases such as image collection, image denoising, and segmentation. Initially, the mammographic images are collected from two benchmark datasets like Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS). Next, image normalization and Contrast-Limited Adaptive Histogram Equalization (CLAHE) techniques are employed for enhancing the visual capability and contrast of the mammographic images. After image denoising, electromagnetism-like (EML) optimization technique is used for segmenting the noncancer and cancer portions from the mammogram image. The proposed EML technique includes the advantages like enhanced robustness to hold the image details and adaptive to local context. Lastly, template matching is carried out after segmentation to detect the cancer regions, and then, the effectiveness of the proposed model is analysed in light of Jaccard coefficient, dice coefficient, specificity, sensitivity, and accuracy. Hence, the proposed model averagely achieved 92.3% of sensitivity, 99.21% of specificity, and 98.68% of accuracy on DDSM dataset, and the proposed model averagely achieved 92.11% of sensitivity, 99.45% of specificity, and 98.93% of accuracy on MIAS dataset.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

Ittannavar¹,

Havaldar

2022

BioMed Research International

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“…Heidari et al [ 48 ] employ a Gaussian bandpass filter to detect suspicious zones using local properties of the image. On the other hand, Suresh et al [ 49 ] and Sapate et al [ 50 ] employ a fuzzy-based strategy to cluster all the pixels with similar features in order to detect all the zones that have differences. Other strategies involve the utilization of mathematical morphology [ 51 , 52 , 53 , 54 , 55 ], image contrast and intensity [ 56 , 57 ], geometrical features [ 58 , 59 ], correlation and convolution [ 60 , 61 ], non-linear filtering [ 62 , 63 ], texture features [ 64 ], deep learning [ 65 , 66 , 67 , 68 , 69 ], entropy [ 70 , 71 ], among other strategies.…”

Section: Image Processing and Classification Strategiesmentioning

confidence: 99%

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Basurto-Hurtado

Cruz-Albarran

Toledano‐Ayala

et al. 2022

Cancers

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Breast cancer is one the main death causes for women worldwide, as 16% of the diagnosed malignant lesions worldwide are its consequence. In this sense, it is of paramount importance to diagnose these lesions in the earliest stage possible, in order to have the highest chances of survival. While there are several works that present selected topics in this area, none of them present a complete panorama, that is, from the image generation to its interpretation. This work presents a comprehensive state-of-the-art review of the image generation and processing techniques to detect Breast Cancer, where potential candidates for the image generation and processing are presented and discussed. Novel methodologies should consider the adroit integration of artificial intelligence-concepts and the categorical data to generate modern alternatives that can have the accuracy, precision and reliability expected to mitigate the misclassifications.

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“…In the case of stiffness and swelling in the breast tissue, clinical breast examination is necessary. Clinical examination is performed by ultrasonography, fine needle biopsy, and mammography techniques [2]. Another diagnostic method is a computer-assisted diagnosis, a decision support system used to determine whether a person has breast cancer.…”

Section: Introductionmentioning

confidence: 99%

“…1 BCC dataset which is taken from UCI Machine Learning Repository, 2 The data includes 99 females with benign breast lesion…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Breast Cancer Using Improved Machine Learning Algorithms Based on Bayesian Optimization

Ceylan¹

2020

ijisae

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Breast cancer is one of the most common types of cancer and is the second main cause of cancer death in females. Early detection of breast cancer is crucial for the survival of a patient as well as for the quality of life throughout cancer treatment. The aim of this study is to develop improved machine learning models for early diagnosis of breast cancer with high accuracy. In this context, a performance comparison of machine learning algorithms including Support Vector Machines, Decision Trees, Naive Bayes, K-Nearest Neighbor, and Ensemble Classifiers was performed on a dataset consisting of routine blood analysis combined with anthropometric measurements to diagnose breast cancer. Neighborhood component analysis was applied as a feature selection method to reveal relevant biomarkers that can be used in breast cancer prediction. In order to assess the performance of each proposed classifier model, two different data division procedures such as hold-out and 10-fold cross-validation were employed. Bayesian Optimization algorithm was applied to all classifiers for the maximizing the prediction accuracy. Different performance criteria such as accuracy, precision, sensitivity, specificity, and Fmeasure were used to measure the success of each classifier. Experimental results showed that the Bayesian optimization-based K-Nearest Neighbor performs better than other machine learning algorithms under the hold-out data division protocol with an accuracy of 95.833%. The results obtained in this study may provide a new perspective on the application of improved machine learning techniques for the early detection of breast cancer.

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Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms

Cited by 24 publications

References 34 publications

[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

[Retracted] Segmentation of Breast Masses in Mammogram Image Using Multilevel Multiobjective Electromagnetism‐Like Optimization Algorithm

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Diagnosis of Breast Cancer Using Improved Machine Learning Algorithms Based on Bayesian Optimization

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