Artificial Intelligence to identify aggressive Breast Cancer

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A team of experts from IIT-Kharagpur (IIT-Kgp) and Tata Medical Centre (TMC), Kolkata, has devised a computer-assisted model they say can automatically grade breast cancer aggressiveness, even in remote settings, providing fresh impetus to AI-based medical technology in India. It also seeks to reduce human error in identifying breast cancer of various levels of aggressiveness to assist in distinguishing normal and low and higher risk malignant tumours. To do that, the team tapped into deep learning, a form of AI concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. "The idea is to assess and identify the cancer that's of high risk. This software allows accurate identification of the aggressive cancers anywhere, even in the remotest part of the country, allowing faster referral and quicker treatment for patients, irrespective of their geographical location," Sanjoy Chatterjee, senior clinical oncologist at TMC, told IANS.


Baidu Research's breast cancer detection algorithm outperforms human pathologists

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Baidu Research today announced it has developed a deep learning algorithm that in initial tests outperforms human pathologists in its ability to identify breast cancer metastasis. The convolutional neural net was trained by splitting 400 large images into grids of tens of thousands of smaller images, then randomly selecting 200,000 of those smaller images. The algorithm then performs analysis to classify each of the smaller photos as well as its neighboring cells. A variety of algorithms have been introduced to help pathologists examine images that can be gigabytes in size by cutting them into smaller parts. Baidu Research's algorithm attempts to move this technique forward by mimicking a pathologist's method to examine the area surrounding a breast cancer tumor cell, at once examining individual cells and nearby cells.


Prediction of Malignant & Benign Breast Cancer: A Data Mining Approach in Healthcare Applications

arXiv.org Machine Learning

As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mortality rate due to breast cancer does need attention, for early detection so that prevention can be done in time. As a potential contributor to state-of-art technology development, data mining finds a multi-fold application in predicting Brest cancer. This work focuses on different classification techniques implementation for data mining in predicting malignant and benign breast cancer. Breast Cancer Wisconsin data set from the UCI repository has been used as experimental dataset while attribute clump thickness being used as an evaluation class. The performances of these twelve algorithms: Ada Boost M 1, Decision Table, J Rip, Lazy IBK, Logistics Regression, Multiclass Classifier, Multilayer Perceptron, Naive Bayes, Random forest and Random Tree are analyzed on this data set. Keywords- Data Mining, Classification Techniques, UCI repository, Breast Cancer, Classification Algorithms


Life Extension Daily News

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Computer algorithms analyzing digital pathology slide images were shown to detect the spread of cancer to lymph nodes in women with breast cancer as well as or better than pathologists, in a new study published online in the Journal of the American Medical Association.1


Toward Efficient Breast Cancer Diagnosis and Survival Prediction Using L-Perceptron

arXiv.org Artificial Intelligence

Breast cancer is the most frequently reported cancer type among the women around the globe and beyond that it has the second highest female fatality rate among all cancer types. Despite all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still unsatisfactory. In this paper, we propose a novel type of perceptron called L-Perceptron which outperforms all the previous supervised learning methods by reaching 97.42 \% and 98.73 \% in terms of accuracy and sensitivity, respectively in Wisconsin Breast Cancer dataset. Experimental results on Haberman's Breast Cancer Survival dataset, show the superiority of proposed method by reaching 75.18 \% and 83.86 \% in terms of accuracy and F1 score, respectively. The results are the best reported ones obtained in 10-fold cross validation in absence of any preprocessing or feature selection.