Malaria Detection Using Deep Learning

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premchand premchand
Navya Laxmi
Roja Ramani


Malaria is one of the deadliest diseases across the globe. This is caused by the bite of female Anopheles mosquito that transmits the Plasmodium parasites. Traditional malaria detection techniques require experts to test blood cells under a microscope. The shortage of skilled technicians and the unavailability of required equipment and infrastructure result in false diagnoses leading to an increase in morality rate. In existing system detection  using Machine learning techniques like Support Vector Machine (SVM) which is tedious and requires hand-engineered features extraction to train data, and the results where not up to the mark. In our proposed system we used deep neural networks to detect the malaria virus in human blood cells. The proposed method shows a system with end-to-end automated models using a deep neural network that performs both feature extraction and classification using blood smear cell images. Models are evaluated based on accuracy, precision, recall and F1-score. Data preprocessing techniques like Data segmentation and Normalization are applied to maximize the model performance and a five layer convolutional network to perform best in class feature extraction.

Index Terms – Deep Neural Network, CNN, Image Data Generator.


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How to Cite
premchand, premchand, Sathwika, Navya Laxmi, and Roja Ramani. “Malaria Detection Using Deep Learning”. Technix International Journal for Engineering Research, vol. 9, no. 6, July 2022, pp. 163-6,
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