Breast Cancer Detection and Classification Using Artificial Neural Networks

Hamad, Yousif; Simonov, Konstantin; Naeem, Mohammad

doi:10.1109/aicis.2018.00022

Cited by 23 publications

(12 citation statements)

References 9 publications

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“…It was not very far when we started solving problems such as pancreas classification [44], prostate cancer classification [39], retinal blood vessel segmentation [11], cell nucleus classification using Mask RCNN [38] there are numerous other researchers where the researchers have incorporated some deep learning technique or convolution neural network algorithm to detect, classify and/or segment the given images we can see them here [19,20,21,22,23,24,25]. When we first started our research all we knew about was that a convolution neural network could be used for things such as classification, soon when we saw its applications then we came to know that it could be implemented to solve some medical imagery based problem, after some research we found some interesting researchers like [10,11,12,13,44] these are the researches that have used convolution neural network to classify and segment the cancerous tumor, though all these researches are quite different than the other, for instance take [13] they use CNN to detect and classify breast cancer tumor in mammogram images, when it comes to preprocessing they have taken out the patches of those mammograms and then they gave those patches to CNN, but when we see other research that work with deep learning technique for achieving breast cancer detect and/or classification then we see them using full images of mammograms along with some other preprocessing technique such as [9,15,18]. While doing our research to find the best fit CNN for us, we came by the U-Net [37] architecture of convolution neural network, these are most widely used for medical image analysis.…”

Section: Literature Reviewmentioning

confidence: 95%

“…In [15] the researcher was able to obtain the data of about 800 breast cancer patients who had underwent a biopsy, with this data he implemented a confusion matrix and ROC analysis, he was able to obtain 90.5% accurate results, classifying the given data into benign, malignant or normal. Another research [18] using artificial neural network and mammogram images trained a Probabilistic Neural Network (PNN) which was able to classify the breast tumor with over 80% accuracy, they have also mentioned other methods to segment and make a bounding box using Fuzzy c-Means (FCM) and Discrete Wavelet Transform (DWT) [18]. But simple artificial neural networks are not made for the image-based data, in fact there's an algorithm called convolution neural network [34] which out performs any other type of algorithm when it comes to image analysis, classification, segmentation and much more.…”

Section: Introductionmentioning

confidence: 99%

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Classification and Segmentation of Breast Tumor Using Mask R- CNN on Mammograms

Raza¹,

Sarwar²,

Syed³

et al. 2021

Preprint

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Purpose Breast cancer has caused more deaths in women compared to any other cancer that might be found among women. With that being said, this research has proposed a method which can detect classify and segment the different types of breast tumors. This paper has also discussed the different methods by which the breast cancer has been classified and segmented in the past. Method Breast cancer can be detected in its early stages by MRI and/or mammography of the breast muscles. For this research a novel approach is proposed for breast cancer detection, classification and segmentation. The proposed framework uses breast mammograms from the CBIS-DDSM (Curated Breast Imaging Subset of DDSM) DICOM images. Mammograms are radio images of a muscle. The DICOM data has been preprocessed in such a way that it could be incorporated with more traditional format, then the patches from the mammogram images have been taken out and finally fed into the Mask RCNN neural network.Results The outcome of the approach is, that the proposed framework is able to localize cancer tumor, even when it has developed in multiple regions, making it a multi-class classifier. The framework is also able to classify whether the tumor is benign or malignant as well as segments the cancerous tumor region with a pixel wise annotation. The average accuracy observed is about 85% on test cases, with precision value of 0.75, recall being 0.8 and F1 score 0.825.Conclusion The proposed framework is cost efficient and can be used as a helping tool for the radiologist in breast cancer detection. In future the proposed approach can also be implemented on other cancerous tumors for classification and segmentation purposes.

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Section: Literature Reviewmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Classification and Segmentation of Breast Tumor Using Mask R- CNN on Mammograms

Raza¹,

Sarwar²,

Syed³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Computer vision software based on machine learning and deep learning algorithms is making automated analysis possible in delivering fast and accurate results. In this regard, image processing plays a crucial role in the diagnosis and detection of cancers as well as in monitoring cancer progression patients [23]. Similarly, machine learning is capable in overcoming limitations in stem cells research, whereby, labbased characterization and classification using chemical reagents and biological assays can be labor-intensive, expensive, and time-consuming as well as less accurate.…”

Section: A Image-based Dataset For Machine Learning In Stem Cells Researchmentioning

confidence: 99%

“…A similar approach is seen in cancer research, whereby, machine learning has been widely applied in the identification and classification of cancer cells. Similar machine learning models and approaches can be applied in stem cells research [21][22][23][24] that could assist in accelerating the evaluation of safety and efficacy of stem cells. This could potentially bring stem cells to the forefront of personalized medicine.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Stem Cells Research: Application for Biosafety and Bioefficacy Assessment

et al. 2021

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The applicability of machine learning-based analysis in the field of biomedical field has been very beneficial in determining the biological mechanism and validation for a wide range of biological scenarios. This approach is also gaining momentum in variou s stem cells research activities, specifically for stem cells characterization and differentiation pattern. The adoption of similar computational approaches to study and assess biosafety and bioefficacy risks of stem cells for clinical application is the n ext progression. In particular where tumorigenicity has been one of the major concerns in stem cells therapy. There are many factors influencing tumorigenicity in stem cells which may be difficult to capture under conventional laboratory settings. In addition, given the possible multifactorial etiology of tumorigenicity, defining a onesize-fits-all strategy to test such risk in stem cells might not be feasible and may compromise stem cells safety and effectiveness in therapy. Given the increase in biological datasets (which is no longer limited to genomic data) and the advancement of health informatics powered by state-of-the-art machine learning algorithms, there exists a potential for practical application in biosafety and bioefficacy of stem cells therap y. Here, we identified relevant machine learning approaches and suggested protocols intended for stem cells research focusing on the possibility of its usage for stem cells biosafety and bioefficacy assessment. Ultimately, generating models that may assist healthcare professionals to make a better-informed decision in stem cell therapy.

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“…The third stage was feature extraction using Discrete Wavelet Transform (DWT). At last, classification of the stage of Breast Tumor into benign, malignant or normal was done using PNN (Probabilistic Neural Network), the findings was a high classification rate of (90%) [8]. Mahfuzah Mustafa et al have conducted a method to improve the Gradient Vector Flow (GVF) Snake Active Contour segmentation technique in mammography segmentation.…”

Section: Litereture Reviewmentioning

confidence: 99%

Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology

Mohammed

Saleh

2021

IJEECS

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<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver operating characteristic ROC). Dataset was downloaded from UCI ml repository; it is composed of 9 attributes and 699 samples. The findings are clearly showing that the RBF NN classifier is the best in prediction of the type of breast tumors since it had recorded the highest performance in terms of correct classification rate (accuracy), sensitivity, specificity, and AUC (area under Receiver Operating Characteristic ROC) among all other models.</p>

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Breast Cancer Detection and Classification Using Artificial Neural Networks

Cited by 23 publications

References 9 publications

Classification and Segmentation of Breast Tumor Using Mask R- CNN on Mammograms

Classification and Segmentation of Breast Tumor Using Mask R- CNN on Mammograms

Machine Learning in Stem Cells Research: Application for Biosafety and Bioefficacy Assessment

Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology

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