Summary of A Diagnostic Model For Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods, by Amir Masoud Rahmani et al.
A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods
by Amir Masoud Rahmani, Parisa Khoshvaght, Hamid Alinejad-Rokny, Samira Sadeghi, Parvaneh Asghari, Zohre Arabi, Mehdi Hosseinzadeh
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed method uses a ResNet-based feature extractor to detect acute lymphoblastic leukemia (ALL) severity by analyzing blast cell characteristics in both bone marrow and peripheral blood samples. The model employs transfer learning from various architectures, including ResNet, VGG, EfficientNet, and DensNet families, followed by the application of feature selectors such as genetic algorithm, principal component analysis, and random forest to extract relevant features. The selected features are then input into a multi-layer perceptron (MLP) classifier, which outperforms other classification models with an accuracy of 90.71% and sensitivity of 95.76%. This technique has the potential to automate and improve the diagnosis of ALL by providing more accurate and efficient results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses AI to help doctors diagnose a type of cancer called acute lymphoblastic leukemia (ALL). Doctors currently have to look at slides of blood cells under a microscope, which is time-consuming and not very accurate. The researchers used special computer models to analyze the blood cell images and detect the severity of ALL. They tested different combinations of these models and found that one combination worked best. This new method can accurately diagnose ALL 90% of the time, which is much better than what doctors can do now. |
Keywords
» Artificial intelligence » Classification » Principal component analysis » Random forest » Resnet » Transfer learning
