The breast cancer dataset from Kaggle is utilized. The test and training data were divided by 7:3. Important features are determined by the correlation matrix. Metrics found the most effective classification models after creating the models. The results of future optimization techniques will be ...
The evaluations are conducted by using machine learning algorithms, Locally Deep SVM, Boosted Decision Tree, Averaged Perception, Bayes Point and Decision Forest to predict Breast Cancer. We conducted an experiment on 18K breast cancer image dataset. A hybrid machine learning algorithm (HMLA) based ...
Dataset 1. https://www.kaggle.com/datasets/aryashah2k/breast-ultrasound-images-dataset. Download references Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Author information Authors and Affiliations Kalam Technical University,...
Dataset preparation and modeling process. Full size image Performance evaluation At this stage, the trained models underwent evaluation on the test set to derive the ultimate estimate of their performance on previously unseen data. The evaluation process encompassed two distinct approaches, as illustrated...
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing ...
The Mammographic Image Analysis Society (MIAS) dataset is a popular database that is frequently used to evaluate the efficacy of algorithms for the identification of breast cancer. To further assess the effectiveness of the suggested models, a specialized dataset from the "Salah Azaiez Hospital," ...
To our knowledge, there have been limited studies addressing both conventional machine learning approaches and deep learning methods for predicting breast cancer survival using non-image data. Building upon existing research (Table 1), this study aimed to develop and compare the DL and ML models ...
Download: Download full-size image Fig. 1. BC diagnosis methods. 2.1. Based on type BC types can usually be split into two main categories: non-invasive and invasive BC. There is also some sub-categorization based on status of the hormone receptors, proteins or the genes of the cancer cel...
Early and accurate diagnosis of breast cancer (BC) using digital mammograms can improve disease detection accuracy. Medical images can be detected, segmented, and classified for the design of computer-aided diagnosis (CAD) models which assist radiologists in accurately diagnosing breast lesions. Therefor...
image dataset is utilized to evaluate the generalization ability of our SAFNet using 5-fold cross-validation. The simulation experiments reveal that the SAFNet can produce higher classification results compared with four existing breastcancer classificationmethods. Therefore, our SAFNet is an accurate ...