The role of artificial intelligence (AI) in diagnostic medical imaging is being explored in depth. AI is increasingly uncovering valuable insights that assist in clinical decision-making, help patients manage their health, and make sense of previously inaccessible unstructured data. It has shown remarkable accuracy and sensitivity in spotting imaging abnormalities, promising to improve how we detect and characterize tissue issues. AI has already proven that it may be a valuableally for radiologists and pathologists looking to accelerate their productivity and potentially improve their accuracy. At the beginning of an AI-assisted diagnostic imaging revolution, the medical community has to anticipate the potential unknowns of this technology to ensure effective and safe incorporation into clinical practice. A careful examination of the potential risks associated with AI, in light of its unique strengths, is crucial for defining its place in medicine. Finding the right balance between improved detection and the risk of overdiagnosis will be a significant challenge. 

This is what drives my passion for studying AI-based machine learning and deep learning techniques to help differentiate between normal and abnormal findings in medical images. 

Right now, I'm working as a Lecturer in Software Engineering Department at Daffodil International University (DIU), Bangladesh. Additionally, I am working as a Research Coordinator at the Health Informatics Research Lab [HIRL], DIU.

Research Interests: Data Science, Data Analytics, Machine learning, Deep learning, Medical image analysis, Computer vision, Segmentation, Detection & Classification, etc. and I have been researching on this field for almost two and a half years. 

Skills & Expertise: Data Science, Machine Learning & Deep Learning, Medical Image Processing, Signal Processing, MS Word, MS Powerpoint, MS Excel, Canva, Latex writing.