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... Among all the available classification methods, random forests provide the highest accuracy. The random forest algorithm can also handle big data with huge number of variables running into thousands. When the data is imbalenced it automatically balance data sets. Random forest also handles variables fast, making it suitable for complicated tasks (Fig. ...
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Breast cancer is one of the most common cancers in afflicted Iraqi women. All studіеs іn recant yеаrs have іndіcatеd thаt Еpstеіn - Bаrr vіrus (ЕBV) іs аssocіatеd wіth cеrtaіn cаncеrs, іncludіng brеаst cancеr. Thе еаrly dеtеctіon of brеаst cаncеr іs thе bеst solutіon to control thе dіsеаsе. Thе purposе of thіs study іs to rеvеаl thе rеlatіonshіp bе...
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... Among the approaches [3], Support Vector Classification, Logistic Regression, and Multilayer Perceptron all perform fairly well. The accuracy ratings for all available approaches are more than 90% [4] even after a significant reduction in the number of characteristics employed. Random Forest, K-Nearest Neighbors, Nave Bayes and Decision Tree [5] are a few of the categorization algorithms now in use. ...
Breast cancer prediction is an important topic in the field of healthcare. Breast cancer is one of the most common cancers in women and early detection is critical for successful treatment. There are several methods for predicting breast cancer, including imaging studies, genetic testing, and risk assessment models. Early detection can greatly improve the chances of successful treatment and long-term survival. One approach to detecting breast cancer is to use machine learning algorithms such as support vector machine (SVM) classifiers. SVMs are a popular type of supervised learning algorithm that can be used for classification or regression analysis. To use SVMs for breast cancer classification, you need to first prepare the data by dividing it into training and testing sets. The training set is used to train the SVM model, and the testing set is used to evaluate the performance of the model. The SVM model learns to classify the data by adjusting the parameters of the kernel function. In this paper, the performance of Linear, Polynomial, Gaussian and Sigmoid machine-learning kernels in the Support Vector Machine method was investigated to determine which kernel classifier is better at diagnosing breast cancer. In addition, this study made usage of the Wisconsin Breast Cancer (Diagnostic) dataset that contains 569 occurrences and 32 features for analysis. The major objective of this study is to compare a variety of kernel classifiers to identify the one that provides the best accuracy. Linear kernel support vector machine was shown to have the highest accuracy (97.90%) and lowest false discovery rates in this investigation. In contrast, other kernels and classification algorithms show low performance, which may not be more accurate in breast cancer prediction.
... R-CNN[17] is a convolutional neural network based on Girshick's region proposal, which he proposed for the first time in 2014[2,10,18]. The main focus of R-CNN is to enhance the standard of BBs of candidates and to extract high-level feature[18] by taking a deep architecture. ...
In the real world, computer vision is used for more challenging tasks like detecting of objects in an image or video. There are multiple applications of object detection in various domains like animation, autonomous driving, monitoring of traffic, communicating through video. With the development of new emerging technologies in deep learning, finding accuracy of objects by performing classification and detection became possible. When compared to traditional object detection methods deep learning methods has an ability of feature learning and rendering. This paper is mainly focused on the working procedure of convolutional neural networks in detecting objects that are present in the environment of an image. CNN, R-CNN, and Faster R-CNN are the main models of deep learning which are considered for comparative-based study. Comparison between these models is made by identifying their accuracies, limitations, and speed. Among the three models, Faster R-CNN is identified as ideal one as it has higher accuracy and less expensive in nature when compared with R-CNN whereas CNN model can be only used for image classification (Tripathi in Journal of Innovative Image Processing (JIIP) 3:100–117, 2021), but it cannot localize the objects.
