A Cognitive Approach for Effective Malaria Detection
Keywords:
Malaria, Data Science CNN, Deep LearningAbstract
Malaria is a lethal disease transmitted through the sting of an infected female anopheles’ mosquito. Malaria is among the most prevalent diseases in the world. There are many drugs available to turn malaria into a curable disease, but we are unable to diagnose and cure it due to inadequate technology and equipment. The diagnostic method for malaria is to manually count parasites and red blood cells, which is long and prone to errors, especially if patients are examined several times a day. This problem can be remedied by teaching robots to do pathologist's work. Many deep learning algorithms can be used to train the system. To categories blood smears into infected and normal, our algorithm uses CNN-based classification. The experimental results reveal that our model performs well on microscopic images, with 95.54 percent accuracy, and that it has reduced model complexity, requiring less computational time. Consequently, it surpasses the prior state of the art.
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