MALARIA DISEASE DETECTION USING DEEP LEARNING

Abstract

Artificial intelligence technology is starting to have a bigger influence in certain industries, like healthcare [1]. There is a lot of effort being done to increase the effectiveness of diagnosing malaria early on, when medical imaging is essential. A parasite that frequently infects a particular kind of mosquito that feeds on people can cause malaria, a dangerous and occasionally deadly disease. A contagious illness An viral disease called malaria kills more than 500,000 people annually, estimated 95% of deaths throughout the world. The majority of these fatalities are brought on by a delayed or inaccurate diagnosis. The manual microscope is now thought to be the best technology for diagnosing malaria. On the other hand, it takes time and is subject to human mistake. It is crucial that the evaluation procedure be automated because it is such a significant issue for world health. Female anopheles mosquitoes transmit the highly contagious disease malaria. Animals as well as humans are harmed by this sickness. In the worst case scenario, this illness could result in the patient's death if it is not adequately diagnosed in the early stages. because of a lack of highly technical knowledge It becomes quite challenging to confirm the existence of sickness in the workplace. In this situation, IT assistance is required for accurate and quick disease identification.. In this paper, a deep learning based technique to detect malaria from several modalities.