Summary of Automated Web-based Malaria Detection System with Machine Learning and Deep Learning Techniques, by Abraham G Taye et al.
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques
by Abraham G Taye, Sador Yemane, Eshetu Negash, Yared Minwuyelet, Moges Abebe, Melkamu Hunegnaw Asmare
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a deep learning approach to detect malaria-infected cells using traditional convolutional neural networks (CNNs) and transfer learning models. The authors formulate a technique that leverages VGG19, InceptionV3, and Xception models, trained on NIH datasets and tested using performance metrics such as accuracy, precision, recall, and F1-score. The results show that deep CNNs achieved the highest accuracy (97%), followed by Xception (95%). Other models, including SVM and Inception-V3, demonstrated lower accuracies (83% and 94%, respectively). Furthermore, the system is accessible through a web interface, enabling users to upload blood smear images for malaria detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to help doctors detect if someone has malaria by looking at pictures of their blood cells. The method uses special kinds of artificial intelligence (AI) called deep learning models. These models are trained on lots of pictures and can recognize patterns that humans might miss. The researchers tested several different AI models and found that one in particular, called Xception, was the best at detecting malaria-infected cells. They also created a website where doctors or others can upload their own blood cell pictures to see if someone has malaria. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Precision » Recall » Transfer learning
