ABSTRACT :
This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones. Methods: We have developed the first deep learning method that can detect malaria parasites in thick blood smear images and can run on smartphones. Our method consists of two processing steps. First, we apply an intensity-based Iterative Global Minimum Screening (IGMS), which performs a fast screening of a thick smear image to find parasite candidates. Then, a customized Convolutional Neural Network (CNN) classifies each candidate as either parasite or background. Together with this paper, we make a dataset of 1819 thick smear images from 150 patients publicly available to the research community. We used this dataset to train and test our deep learning method, as described in this paper. Results: A patient-level five-fold cross-evaluation demonstrates the effectiveness of the customized CNN model in discriminating between positive (parasitic) and negative image patches in terms of the following performance indicators: accuracy (93.46% ± 0.32%), AUC (98.39% ± 0.18%), sensitivity (92.59% ± 1.27%), specificity (94.33% ± 1.25%), precision (94.25% ± 1.13%), and negative predictive value (92.74% ± 1.09%). High correlation coefficients (>0.98) between automatically detected parasites and ground truth, on both image level and patient level, demonstrate the practicality of our method. Conclusion: Promising results.
EXISTING SYSTEM :
In recent years, several approaches have been proposed for image processing and analysis on both thin and thick blood smears, aiming at automated detection of parasites. Reviews of the published literature may be found in [5], [8], [9]. In the following paragraph, we provide a brief overview of the approaches for malaria detection in thick blood smears.
Traditional parasite detection techniques are often performed based on segmentation [10]–[13] using thresholding and morphological operations. Kaewkamnerd et al. [10] propose a method using an adaptive threshold on the V-value histogram of the HSV image to extract parasite candidates and white blood cells (WBCs) from the background, and then distinguish parasites from WBCs according to their size. Evaluation on 20 images shows that the proposed method achieves an accuracy of 60%. Hanif et al. [11] use an intensity-stretching method to enhance the contrast of 255 thick blood smears, and then use an empirical threshold to segment malaria parasites.
DISADVANTAGES OF EXISTING SYSTEM :
1) Less accuracy
2)low Efficiency
PROPOSED SYSTEM :
Splitting our problem into a screening and classification step allows faster processing because we only need to predict on a relatively small number of pixel patches, which reduces the overall processing cost.
ADVANTAGES OF PROPOSED SYSTEM :
1) High accuracy
2)High efficiency
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
