AI DRIVEN BLOOD CELL ANALYSIS FOR MALARIA AND BLOOD CANCER SCREENING
Mrs.P.Gokila1 , Mr.Madeshwaran2 , Nandhini.N3 , Priyadharshini.S4, Sakthi.S5 , Sanjay.B6
1 Assistant Professor , Department of Computer Science and Engineering , Info Institute of Engineering.
2 Assistant Professor , Department of Information Technology , Info Institute
of Engineering.
3,4,5,6 UG Students , Department of Computer Science and Engineering Info Institute of Engineering.
Abstract - Malaria and blood Cancer is one of the deadliest diseases cross the globe. This is caused by the bite of female Anopheles mosquito that transmits the Plasmodium parasites. Some current malaria detection techniques include manual microscopic examination and RDT. These approaches are vulnerable to human mistakes. Early detection of malaria can help in reducing the death rates across the globe.Deep Learning can emerge as a highly beneficial solution in the diagnosis of disease. This model gives a faster and cheaper method for detecting plasmodium parasites. It Was Designed to Identify Malaria Parasites and Cancerous Cell Presence in Blood Using Images of Blood Samples which got Tested with Giemsa-stain. Convolutional neural networks (CNNs) are then trained on these extracted features to accurately classify blood cell images into disease positive or disease-negative categories. The CNN detects malaria parasites in microscopic images by classifying them into parasitized and healthy cells, enabling the detection of malaria parasites. The proposed model consists of three convolutional layers and fully connected layers each. The neural network presented is a cascade of several convolutional layers having multiple filters present in layers, which yields the exceptionally good accuracy as per the available resources. The experiment results demonstrate a significant improvement in malaria parasite and blood cancer recognition. Compared to traditional manual microscopy, the proposed system is more accurate and faster. Finally, this study demonstrates the need to provide robust and efficient solutions by leveraging state-of-the-art technologies to combat malaria and blood cancer.
Keywords : Deep Learning, CNN, Image analysis , InceptionresnetV2
