Thalassemia is a blood disorder and requires careful diagnosis to achieve appropriate treatment. Historically, the responsibility of analysing haemoglobin electrophoresis images has been carried out by highly proficient haematologists. This existing method is time-consuming and requires specialised knowledge. The study aims to develop a solution that employs machine vision techniques Artificial Intelligence etc. and is specifically designed for haemoglobin electrophoresis investigation. The process involves a systematic approach of resizing the images, enhancing their quality, and subsequently performing precise segmentation for eventual diagnosis. The efficacy of the suggested methodology was validated through experimentation on a substantial dataset comprising 72 strips and 576 test strip images These experiments highlight the importance of the technique implemented in this paper by showcasing its efficacy. The approach employed successfully fulfils the objectives of prompt treatment and enhanced patient thalassemia identification up to 97.36% while constituting a notable technological breakthrough in diagnosing thalassemia.

Index Terms- Thalassemia diagnosis, Machine Vision, Haemoglobin electrophoresis, medical technology