Name
Title | ||
|---|---|---|
An e-Health care services framework for the detection and classification of breast cancer in breast cytology images as an IoMT application |
Abstract | ||
|---|---|---|
One of the primary causes of mortality among women aged 20–59 worldwide is breast cancer. Early detection and getting proper treatment can reduce the rate of morbidity of breast cancer. In this paper, we proposed a framework which combines machine learning and computational intelligence-based approaches in e-Health care service as an application of the Internet of Medical Things (IoMT) technology, for the early detection and classification of malignant cells in breast cancer. In the proposed approach, the detection of malignant cells is achieved by extracting various shapes and textured based features, whereas the classification is performed using three well-known classification algorithms. The most innovative part of the proposed approach is the use of Evolutionary Algorithms (EA) for the selection of optimal features, which reduces the computational complexity and accelerates the classification process in cloud-based e-Health care service. Similarly, an ensemble based classifier is used to select the best classifier by adopting the majority voting technique. The performance of the proposed approach is validated through experiments on real data sets which provide an accuracy of 98.0% in the detection and classification of malignant cells in breast cytology images. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.future.2019.01.033 | Future Generation Computer Systems |
Keywords | Field | DocType |
Breast cancer,Internet of Medical Things (IoMT),e-Health,Support Vector Machine (SVM),Naïve Bayesian (NB),Random Forest (RF),Genetic Algorithm (GA),Chain-like Agent Genetic Algorithm (CAGA) | Health care,Data set,Breast cancer,Computational intelligence,Evolutionary algorithm,Computer science,Real-time computing,Artificial intelligence,Classifier (linguistics),Statistical classification,Machine learning,Computational complexity theory | Journal |
Volume | ISSN | Citations |
98 | 0167-739X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sana Ullah Khan | 1 | 0 | 0.34 |
| Naveed Islam | 2 | 37 | 6.58 |
| Zahoor Jan | 3 | 104 | 6.78 |
| Ikram Ud Din | 4 | 79 | 16.80 |
| Atif Khan | 5 | 0 | 1.01 |
| Yasir Faheem | 6 | 36 | 4.78 |