Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a method to detect unlabeled malaria parasites in red blood cells. The current “gold standard” for malaria diagnosis is microscopic examination of thick blood smear, a time consuming process requiring extensive training. Our goal is to develop an automate process to identify malaria infected red blood cells. Major issues in automated analysis of microscopy images of unstained blood smears include overlapping cells and oddly shaped cells. Our approach creates robust templates to detect infected and uninfected red cells. Histogram of Oriented Gradients (HOGs) features are extracted from templates and used to train a classifier offline. Next, the ViolaJones object detection framework is applied to detect infected and uninfected red cells and the image background. Results show our approach out-performs classification approaches with PCA features by 50% and cell detection algorithms applying Hough transforms by 24%. Majority of related work are designed to automatically detect stained parasites in blood smears where the cells are fixed. Although it is more challenging to design algorithms for unstained parasites, our methods will allow analysis of parasite progression in live cells under different drug treatments.
['Zheng Zhang', 'L.L. Sharon Ong', 'Kong Fang', 'Athul Matthew', 'Justin Dauwels', 'Ming Dao', 'H. Harry Asada']
I am writing a research paper on a idea Image classification of unlabeled malaria parasites in red blood cells
[{"from": "human", "value": "I am writing a research paper on a idea Image classification of unlabeled malaria parasites in red blood cells"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a method to detect unlabeled malaria parasites in red blood cells. The current \u201cgold standard\u201d for malaria diagnosis is microscopic examination of thick blood smear, a time consuming process requiring extensive training. Our goal is to develop an automate process to identify malaria infected red blood cells. Major issues in automated analysis of microscopy images of unstained blood smears include overlapping cells and oddly shaped cells. Our approach creates robust templates to detect infected and uninfected red cells. Histogram of Oriented Gradients (HOGs) features are extracted from templates and used to train a classifier offline. Next, the ViolaJones object detection framework is applied to detect infected and uninfected red cells and the image background. Results show our approach out-performs classification approaches with PCA features by 50% and cell detection algorithms applying Hough transforms by 24%. Majority of related work are designed to automatically detect stained parasites in blood smears where the cells are fixed. Although it is more challenging to design algorithms for unstained parasites, our methods will allow analysis of parasite progression in live cells under different drug treatments."}]