Disease diagnoses could be sometimes very easy tasks, while others may be a bit trickier. The traditional methods which are used to diagnose a disease are manual and error-prone. But early detection and prevention can significantly reduce the chances of death. Breast Cancer (BC) is a common cancer for women around the world, and early detection of BC can greatly improve prognosis and survival chances by promoting clinical treatment to patients early. <> Usage of Artificial Intelligence (AI) predictive techniques enables auto diagnosis and reduces detection errors compared to exclusive human expertise. The multi pre-processed data were assessed for breast cancer's risk and diagnosis using SVM. DOI: 10.1109/ACCESS.2019.2892795 Corpus ID: 68066662. Overall cancer incidence trends (13 oldest SEER registries) are stable in women, but declining by 3.1% per year in men (from 2009-2012), much of which is because of recent rapid declines in prostate cancer diagnoses. MLP achieved the lowest accuracy rates regardless the MD mechanism/percentage. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. This study evaluates the influence of MD on three classifiers: Decision tree C4.5, Support vector machine (SVM), and Multi-Layer Perceptron (MLP). Dept. A detailed analysis of those articles was conducted in order to classify most used AI techniques for The network was trained and validated on 80 % tissue images and 20 % for testing. have reviewed the current literature for the last 10 years, from January 2009 to December 2019. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. In this study, the proposed convolutional neural network (AlexNet) approach to extract the deepest features from the BreaKHis dataset to diagnose breast cancer as either benign or malignant. A critical unmet medical need is distinguishing triple negative breast cancer, the most aggressive and lethal form of breast cancer, from non-triple negative breast cancer. Despite this progress, death rates are increasing for cancers of the liver, pancreas, and uterine corpus, and cancer is now the leading cause of death in 21 states, primarily due to exceptionally large reductions in death from heart disease. 10 No. The proposed system obtained accuracy, sensitivity, specificity, and AUC, 95 %, 97 %, 90 % and 99.36 % respectively. BC-RAED) that is capable of accurately establishing BCa at the early stage. Join ResearchGate to discover and stay up-to-date with the latest research from leading experts in, Access scientific knowledge from anywhere. Moreover, artificial neural networks, support vector machines and ensemble classifiers performed better than the other techniques, with median accuracy values of 95%, 95% and 96% respectively. this learning and they have been used to classify colon cancer cells.20,21 K-nearest neighbors (KNN) unsupervised learning also has been applied to breast cancer data.12 Due to the large number of genes, high amount of noise in the gene expression data, and also the complexity of biological networks, there is a need to deeply analyze the raw data Nonetheless, the disease remains as one of the deadliest disease. <> Among children and adolescents (aged birth-19 years), brain cancer has surpassed leukemia as the leading cause of cancer death because of the dramatic therapeutic advances against leukemia. The best accuracy achieved by applying this procedure on the new dataset was 89.8876%. S.-W. Chang, S. Abdul-Kareem, A.F. For performance evaluation and validation, the proposed methods were applied to independent gene expression datasets. We also demonstrate that naive Bayes works well for certain nearly-functional feature dependencies, thus reaching its best performance in two opposite cases: completely independent features (as expected) and function-ally dependent features (which is surprising). Mortality data were collected by the National Center for Health Statistics. Authors compared these tools on some given factors like correctly classified accuracy, in-correctly classified accuracy and time by applying four algorithms i.e. The traditional methods which are used to diagnose a The In this paper, we Before the deep learning revolution, machine learning approaches including the Cancer Detection using Image Processing and Machine Learning. Breast cancer is sometimes found after symptoms appear, but many women with breast cancer have no symptoms. In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. We performed a systematic literature review (SLR) of 176 selected studies published between January 2000 and November 2018. Here, a common misconception, Missing Data (MD) is a common drawback when applying Data Mining on breast cancer datasets since it affects the ability of the Data mining classifier. Significant effort has been put forth for breast cancer (BC) recognition from histological images in the last decade, where most efforts are made to classify the two fundamental types of breast cancer (benign and malignant) using Computer Aided Diagnosis (CAD). Usage of Artificial Intelligence (AI) predictive techniques enables This Python project with tutorial and guide for developing a code. Dept. 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