A5101436071- Cutaneous Melanoma Detection and Management
- Online Learning and Analytics
- Mosquito-borne diseases and control
- Dengue and Mosquito Control Research
- Digital Imaging for Blood Diseases
- Nonmelanoma Skin Cancer Studies
- Imbalanced Data Classification Techniques
- Machine Learning in Healthcare
- Cutaneous lymphoproliferative disorders research
- Artificial Intelligence in Healthcare
SASTRA University
2025
Dr. M.G.R. Educational and Research Institute
2022-2023
Adithya Institute of Technology
2023
Diabetes is one of the most devastating diseases and affects many people. can be caused by a variety causes, including ageing, obesity, inactivity, genetics, poor diet, high blood pressure, others. increases likelihood developing several illnesses, heart disease, renal stroke, eye problems, nerve damage, etc. The information needed to diagnose diabetes currently gathered through tests used in hospitals, diagnosis then determine best course treatment. healthcare sector has considerable...
The number of people diagnosed with skin cancer is increasing sharply. Both invasive and non-invasive methods examination may be used to investigate it. However, the method more difficult for patient because samples must taken from lesion itself, or whole cut out. It also requires time cost. To avoid procedures, computer-based analysis diagnosis have potential increase diagnostic accuracy turnaround time. This study develops a unique discriminative deep learning architecture (DDLA)...
Summary Dengue virus infection is one of the major worldwide health issues and a substantial epidemic infectious human disease. More than 2 billion humans live in dengue susceptible regions with an annual mortality rate about 5%–20%. At initial stages, it difficult to differentiate fever symptoms from other similar diseases. Therefore, early diagnosis disease can help protecting lives by making preventive move before turns into Although detection critical reducing deaths, accurate requires...
The incidence of skin cancer is rapidly increasing worldwide. relevance Skin Cancer Diagnosis (SCD) and the difficulty in achieving an accurate consistent diagnosis have resulted significant research interest. Furthermore, automated detection or classification would be even more helpful a diagnostic assistance system. This study develops efficient Dermoscopic Image Classification Network (DermICNet) for SCD. proposed DermICNet deep learning architecture with arrangement eight convolutional...