International Journal of Science, Engineering and TechnologyWater Quality Monitoring System Using Machine Learning
Authors- Kamalraj S, Professor Dr. V. Sumalatha
Abstract--A histopathological analysis performed by pathologists plays a key role in the diagnosis of breast cancer. A novel approach based on an image processing technique is proposed to help pathologists efficiently produce an accurate diagnosis that is composed of two modules, namely, anomaly detection with a support vector machine (ADSVM) method and a resolution adaptive network (RANet) model. The ADSVM method screens mislabelled patches to improve the training performance of the RANet model. In the RANet model, subnetworks with variable resolutions and depths are utilized to classify images according to the classification difficulty. This adaptive mechanism potentially increases the computational efficiency and prediction accuracy. The proposed CNN with efficient B0 algorithm approach is evaluated using the public datasets: the BreakHis dataset. Binary and multiclass classifications of patient and image levels at different magnification factors are conducted on the BreakHis dataset. The proposed approach achieves significant improvements in both the classification accuracy and computational efficiency.
DOI: /10.61463/ijset.vol.13.issue3.161
