Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation.
Simple Summary: Leukemia is a type of cancer that affects white blood cells and can lead to serious health problems and death. Diagnosing leukemia is currently performed through a combination of morphological and molecular criteria, which can be time-consuming and, in some cases, unreliable. Compute...
| Publicado en: | Cancers Vol. 15; no. 13; pp. 3376 - 3398 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
MDPI
Jul2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164924326&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164924326 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jul2023 vid: 15 iid: 13 pid: 97109 pub: MDPI artinfo: ui: 164924326 164924326 164924326 10.3390/cancers15133376 164924326 ppf: 3376 ppct: 22 formats: tig: atl: Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation. aug: au: Diaz Resendiz, Jose Luis Ponomaryov, Volodymyr Reyes Reyes, Rogelio Sadovnychiy, Sergiy affil: Instituto Politecnico Nacional, Escuela Superior de Ingenieria Mecanica y Electrica–Culhuacan, Av. Sta. Ana 1000, Mexico City 04440, Mexico sug: subj: Computer-Aided Design Leukemia, Lymphocytic, Acute Classification Image Processing, Computer Assisted Leukocytes Artificial Intelligence Neural Networks (Computer) Human Leukemia, Lymphocytic, Acute Epidemiology Leukemia, Lymphocytic, Acute Mortality Cell Nucleus Algorithms Deep Learning Physicians Leukemia, Lymphocytic, Acute Diagnosis Treatment Outcomes Diagnosis, Computer Assisted Cluster Analysis ab: Simple Summary: Leukemia is a type of cancer that affects white blood cells and can lead to serious health problems and death. Diagnosing leukemia is currently performed through a combination of morphological and molecular criteria, which can be time-consuming and, in some cases, unreliable. Computer-aided diagnosis (CAD) systems based on deep-learning methods have shown promise in improving diagnosis efficiency and accuracy. However, these systems suffer from the "black box problem," which can lead to incorrect classifications. This research proposes a novel deep-learning approach with visual explainability for ALL diagnoses based on robust white blood cell nuclei segmentation to provide a highly reliable and interpretable classification. The aim is to develop a CAD system that can assist physicians in diagnosing leukemia more efficiently, potentially improving patient outcomes. The findings of this research may impact the research community by providing a more reliable and explainable deep-learning-based approach to blood disorder diagnosis. Leukemia is a significant health challenge, with high incidence and mortality rates. Computer-aided diagnosis (CAD) has emerged as a promising approach. However, deep-learning methods suffer from the "black box problem", leading to unreliable diagnoses. This research proposes an Explainable AI (XAI) Leukemia classification method that addresses this issue by incorporating a robust White Blood Cell (WBC) nuclei segmentation as a hard attention mechanism. The segmentation of WBC is achieved by combining image processing and U-Net techniques, resulting in improved overall performance. The segmented images are fed into modified ResNet-50 models, where the MLP classifier, activation functions, and training scheme have been tested for leukemia subtype classification. Additionally, we add visual explainability and feature space analysis techniques to offer an interpretable classification. Our segmentation algorithm achieves an Intersection over Union (IoU) of 0.91, in six databases. Furthermore, the deep-learning classifier achieves an accuracy of 99.9% on testing. The Grad CAM methods and clustering space analysis confirm improved network focus when classifying segmented images compared to non-segmented images. Overall, the proposed visual explainable CAD system has the potential to assist physicians in diagnosing leukemia and improving patient outcomes. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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