Affiliations: [a] Department of Computer Science and Engineering, Knowledge Institute of Technology, Salem, Tamilnadu, India
| [b] Department of Information Technology, Kongunadu College of Engineering and Technology, Thollupatti, Tiruchirappali, Tamilnadu, India
| [c] Department of Computer Science, Arulmigu Arthanareeswarar Arts and Science College, Tiruchengode, Tamilnadu, India
Correspondence:
[*]
Corresponding author. P. Vijayalakshmi, Associate Professor, Department of CSE, Knowledge Institute of Technology, Salem, Tamilnadu, India. E-mail: viji.vietw@gmail.com.
Abstract: Adenomyosis is an abnormality in the uterine wall of women that adversely affects their normal life style. If not treated properly, it may lead to severe health issues. The symptoms of adenomyosis are identified from MRI images. It is a gynaecological disease that may lead to infertility. The presence of red dots in the uterus is the major symptom of adenomyosis. The difference in the extent of these red dots extracted from MRI images shows how significant the deviation from normality is. Thus, we proposed an entroxon-based bio-inspired intelligent water drop back-propagation neural network (BIWDNN) model to discover the probability of infertility being caused by adenomyosis and endometriosis. First, vital features from the images are extracted and segmented, and then they are classified using the fuzzy C-means clustering algorithm. The extracted features are then attributed and compared with a normal person’s extracted attributes. The proposed BIWDNN model is evaluated using training and testing datasets and the predictions are estimated using the testing dataset. The proposed model produces an improved diagnostic precision rate on infertility.
Keywords: Medical image processing, adenomyosis, endometriosis, infertility, BIWDNN
