My research focuses on artificial intelligence, deep learning, computer vision, and medical image analysis, with particular emphasis on developing CNN-, transformer-, and attention-based architectures for image classification, segmentation, and computer-aided diagnosis. My work covers diverse real-world applications, including skin cancer detection, brain tumor analysis, COVID-19 diagnosis, agricultural disease detection, and structural damage assessment. My research interests include multi-scale and contextual feature learning, local-global feature fusion, attention mechanisms, transfer learning, ensemble learning, and hybrid deep learning frameworks aimed at improving model accuracy, generalizability, and reliability. I have published extensively in international peer-reviewed journals and actively serve as a reviewer for international journals in artificial intelligence, computer vision, medical imaging, and related interdisciplinary fields. My research aims to advance reliable and effective AI-driven visual analysis systems while fostering international scientific collaboration.