Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images.
In the realm of medical diagnostics, the utilization of deep learning techniques, notably in the context of radiology images, has emerged as a transformative force. The significance of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), lies in their capacity to...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 1; pp. 308 - 339 |
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| Autores principales: | , |
| Formato: | algorithm computer program diagnostic images equations & formulas pictorial review Journal Article |
| Publicado: |
Springer Nature
Feb2024
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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=175966509&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175966509 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2024 vid: 37 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175966509 175966509 175966509 10.1007/s10278-023-00916-8 175966509 ppf: 308 ppct: 31 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images. aug: au: haque, Sheikh Burhan ul Zafar, Aasim affil: https://ror.org/03kw9gc02 Department of Computer Science, Aligarh Muslim University, Uttar Pradesh, 202002, Aligarh, India sug: subj: Diagnostic Imaging Image Processing, Computer Assisted Deep Learning Utilization COVID-19 Diagnosis Models, Theoretical Utilization Conceptual Framework Neural Networks (Computer) Diagnostic Errors Risk Factors Lung Radiography Radiography, Thoracic Tomography, X-Ray Computed Lung Diseases Classification Reliability Algorithms Utilization ab: In the realm of medical diagnostics, the utilization of deep learning techniques, notably in the context of radiology images, has emerged as a transformative force. The significance of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), lies in their capacity to rapidly and accurately diagnose diseases from radiology images. This capability has been particularly vital during the COVID-19 pandemic, where rapid and precise diagnosis played a pivotal role in managing the spread of the virus. DL models, trained on vast datasets of radiology images, have showcased remarkable proficiency in distinguishing between normal and COVID-19-affected cases, offering a ray of hope amidst the crisis. However, as with any technological advancement, vulnerabilities emerge. Deep learning-based diagnostic models, although proficient, are not immune to adversarial attacks. These attacks, characterized by carefully crafted perturbations to input data, can potentially disrupt the models' decision-making processes. In the medical context, such vulnerabilities could have dire consequences, leading to misdiagnoses and compromised patient care. To address this, we propose a two-phase defense framework that combines advanced adversarial learning and adversarial image filtering techniques. We use a modified adversarial learning algorithm to enhance the model's resilience against adversarial examples during the training phase. During the inference phase, we apply JPEG compression to mitigate perturbations that cause misclassification. We evaluate our approach on three models based on ResNet-50, VGG-16, and Inception-V3. These models perform exceptionally in classifying radiology images (X-ray and CT) of lung regions into normal, pneumonia, and COVID-19 pneumonia categories. We then assess the vulnerability of these models to three targeted adversarial attacks: fast gradient sign method (FGSM), projected gradient descent (PGD), and basic iterative method (BIM). The results show a significant drop in model performance after the attacks. However, our defense framework greatly improves the models' resistance to adversarial attacks, maintaining high accuracy on adversarial examples. Importantly, our framework ensures the reliability of the models in diagnosing COVID-19 from clean images. pubtype: Academic Journal doctype: algorithm computer program diagnostic images equations & formulas pictorial review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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