Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images

Dr. D. Jude Hemanth is currently working as a professor in Department of ECE, Karunya University, Coimbatore, India. He also holds the position of “Visiting Professor” in Faculty of Electrical Engineering and Information Technology, University of Oradea, Romania. He also serves as the “Research Scientist” of Computational Intelligence and Information Systems (CI2S) Lab, Argentina; LAPISCO research lab, Brazil; RIADI Lab, Tunisia; Research Centre for Applied Intelligence, University of Craiova, Romania and e-health and telemedicine group, University of Valladolid, Spain.Dr. Hemanth received his B.E degree in ECE from Bharathiar University in 2002, M.E degree in communication systems from Anna University in 2006 and Ph.D. from Karunya University in 2013. He has published 37 edited books with reputed publishers such as Elsevier, Springer and IET. His research areas include Computational Intelligence and Image processing. He has authored more than 200 research papers in reputed SCIE indexed International Journals and Scopus indexed International Conferences.

• 1. Mammogram Data Analysis: Trends, Challenges, and Future Directions2. AI in Breast Imaging: Applications, Challenges and Future Research3. Prediction of Breast Cancer Diagnosis Using a Random Forest Classifier4. Medical Image Analysis of masses in Mammography using Deep Learning model for Earlier Diagnosis of Cancer Tissues5. A framwork for breast cancer diagnostics based on MobileNetV2 and LSTM-based deep learning6. Autoencoder based dimensionality reduction in 3D breast images for efficient classification with processing by deep learning architectures7. Prognosis of breast cancer using machine learning classifiers8. Breast cancer diagnosis through microcalcification9. Scutinization of Mammogram Images using deep learning10. Computational Techniques for Analysis of Breast Cancer Using Molecular Breast Imaging11. Machine learning and deep learning techniques for breast cancer detection using ultrasound imaging12. Efficient Transfer Learning Techniques for Breast Cancer Histopathological Image Classification13. Classification of breast cancer histopathological images based on shape and texture attributes with ensemble machine learning methods14. An automatic level set segmentation of breast Tumor from mammogram images using optimized Fuzzy c-means clustering