METHOD FOR DETECTING MALARIA IN BLOOD SAMPLES

Abstract

ABSTRACT A method (100) for detecting malaria in blood samples. Further, the method comprising acquiring digital images of blood smears from a dataset containing labeled samples of infected and uninfected cells. Further, the method (100) comprising the steps of pre-processing the acquired images to enhance their quality for analysis. Further, the method (100) comprising the steps of extracting relevant features from the pre-processed images using a convolutional neural network (CNN). Further, the method (100) comprising the steps of training the CNN model on the extracted features to classify the images as containing malaria parasites or not. Further, the method (100) comprising the steps of evaluating the trained model's performance using accuracy metrics to ensure reliable malaria detection. <>

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of medical imaging and diagnosis, and in particular relates to a method for detecting malaria using machine learning and computer vision techniques to analyze digital images of blood smears, providing an accurate and efficient diagnosis of malaria infections.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Malaria remains a significant global health challenge, affecting millions of people annually, particularly in tropical and subtropica , Claims:1. A method (100) for detecting malaria in blood samples, the method comprising the steps of:
acquiring digital images of blood smears from a dataset containing labeled samples of infected and uninfected cells;
pre-processing the acquired images to enhance their quality for analysis;
extracting relevant features from the pre-processed images using a convolutional neural network (CNN);
training the CNN model on the extracted features to classify the images as containing malaria parasites or not; and
evaluating the trained model's performance using accuracy metrics to ensure reliable malaria detection.

2. The method (100) as claimed in claim 1, wherein the pre-processing step includes techniques such as image normalization, noise reduction, and background removal to improve the quality of the acquired digital images before feature extraction.

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