MALARIA IDENTIFICATION FROM MICROSCOPIC BLOOD SMEARS : HYBRID DEEP LEARNING
Ayesha Fatima, Dr C. Berin Jones · International journal of engineering science and advanced technology. · 2025
With thousands of fatalities annually, malaria, a deadly illness spread by mosquitoes, continues to be a significant public health concern.Its high mortality rate is a result of its limited access to trustworthy detection techniques as well as issues like inadequate laboratory resources and unskilled staff.The image analysis of red blood cells (RBCs) infected with malaria has recently advanced, offering prospective substitutes for easier detection techniques.In order to create workable solutions that can increase diagnostic accessibility and accuracy, researchers are utilizing digital microscopy and cutting-edge machine learning techniques.Faster response times in clinical settings are made possible by this method, which also shows promise for integration with IoT-enabled devices, allowing for broader deployment in areas with limited resources.These developments highlight how image-based techniques for detecting malaria may improve early diagnosis and treatment, particularly in places with inadequate access to healthcare.