Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method.
Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method.
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DOI：
10.1155/2022/5140148
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发表时间：
2022
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中科院分区：
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白色血细胞（WBC）是作为免疫系统的一部分对抗感染和疾病的血细胞。它们也被称为“防御细胞”。但是血液中白细胞数量的不平衡可能是危险的。白血病是由免疫系统中白细胞过多引起的最常见的血液癌症。急性淋巴细胞白血病（ALL）通常发生在骨髓产生许多未成熟的白细胞并破坏健康细胞时。所有年龄段的人，包括儿童和青少年，都可能受到ALL的影响。非典型淋巴细胞的快速增殖会导致新生血细胞减少，增加患者死亡的机会。因此，早期和精确的癌症检测可以帮助更好的治疗和更高的生存概率。然而，诊断ALL是耗时和复杂的，手动分析是昂贵的，具有主观和容易出错的结果。因此，可靠和准确地检测正常和恶性细胞至关重要。因此，使用计算机辅助诊断模型的自动检测可以帮助医生有效地检测早期白血病。整个方法可以使用图像处理技术自动化，减少医生的工作量并提高诊断准确性。深度学习（DL）对医学研究的影响最近被证明是非常有益的，为医疗保健领域的诊断技术提供了新的途径和可能性。然而，要在深度学习中尽快实现这一点，整个社区必须克服可解释性的限制。由于黑箱操作在人工智能（AI）模型决策中的缺陷，结果缺乏责任和信任。但可解释人工智能（XAI）可以通过解释AI系统的预测来解决这个问题。这项研究强调了白血病，特别是ALL。所提出的策略将急性淋巴细胞白血病识别为应用不同迁移学习模型对ALL进行分类的自动化过程。因此，使用局部可解释模型不可知解释（LIME）来确保有效性和可靠性，该方法还解释了特定分类的原因。该方法在InceptionV3模型下实现了98.38%的准确率。在不同的迁移学习方法之间找到了实验结果，包括ResNet101V2，VGG19和InceptionResNetV2，后来用XAI的LIME算法进行了验证，其中所提出的方法表现最好。所获得的结果及其可靠性表明，它可以优先用于识别ALL，这将有助于医学检查人员。
White blood cells (WBCs) are blood cells that fight infections and diseases as a part of the immune system. They are also known as “defender cells.” But the imbalance in the number of WBCs in the blood can be hazardous. Leukemia is the most common blood cancer caused by an overabundance of WBCs in the immune system. Acute lymphocytic leukemia (ALL) usually occurs when the bone marrow creates many immature WBCs that destroy healthy cells. People of all ages, including children and adolescents, can be affected by ALL. The rapid proliferation of atypical lymphocyte cells can cause a reduction in new blood cells and increase the chances of death in patients. Therefore, early and precise cancer detection can help with better therapy and a higher survival probability in the case of leukemia. However, diagnosing ALL is time-consuming and complicated, and manual analysis is expensive, with subjective and error-prone outcomes. Thus, detecting normal and malignant cells reliably and accurately is crucial. For this reason, automatic detection using computer-aided diagnostic models can help doctors effectively detect early leukemia. The entire approach may be automated using image processing techniques, reducing physicians' workload and increasing diagnosis accuracy. The impact of deep learning (DL) on medical research has recently proven quite beneficial, offering new avenues and possibilities in the healthcare domain for diagnostic techniques. However, to make that happen soon in DL, the entire community must overcome the explainability limit. Because of the black box operation's shortcomings in artificial intelligence (AI) models' decisions, there is a lack of liability and trust in the outcomes. But explainable artificial intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This study emphasizes leukemia, specifically ALL. The proposed strategy recognizes acute lymphoblastic leukemia as an automated procedure that applies different transfer learning models to classify ALL. Hence, using local interpretable model-agnostic explanations (LIME) to assure validity and reliability, this method also explains the cause of a specific classification. The proposed method achieved 98.38% accuracy with the InceptionV3 model. Experimental results were found between different transfer learning methods, including ResNet101V2, VGG19, and InceptionResNetV2, later verified with the LIME algorithm for XAI, where the proposed method performed the best. The obtained results and their reliability demonstrate that it can be preferred in identifying ALL, which will assist medical examiners.
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DOI：
10.3390/s21248424
发表时间：
2021-12-17
期刊：
Sensors (Basel, Switzerland)
影响因子：
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作者：
Hijazi H;Abu Talib M;Hasasneh A;Bou Nassif A;Ahmed N;Nasir Q
通讯作者：
Nasir Q
影响因子：
--
作者：
Pansombut, Tatdow;Wikaisuksakul, Siripen;Phon-on, Aniruth
通讯作者：
Phon-on, Aniruth
影响因子：
--
作者：
Lavric, Alexandru;Valentin, Popa
通讯作者：
Valentin, Popa
DOI：
10.3390/diagnostics10121064
发表时间：
2020-12-08
期刊：
Diagnostics (Basel, Switzerland)
影响因子：
--
作者：
Kasani PH;Park SW;Jang JW
通讯作者：
Jang JW
影响因子：
3.9
作者：
Sharif, Muhammad;Amin, Javaria;Kadry, Seifedine
通讯作者：
Kadry, Seifedine