Advances in Thermal Imaging: A Convolutional Neural Network Approach for Improved Breast Cancer Diagnosis

Ikechukwu, Agughasi Victor; Bhimshetty, Sampoorna; R, Deepu; Mala, M.V

doi:10.1109/icdcot61034.2024.10515323

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…For both the pre-training and fine-tuning stages, a learning rate of 0.0001 is established, which undergoes a 7% decay with each epoch, and a batch size of 64. The choice of parameters in the model was based on extensive experimentation and prior research in the field [50], [51]. The Adam optimizer is employed in conjunction with the binary cross-entropy loss function.…”

Section: Adapted Technique Via Transfer Learningmentioning

confidence: 99%

Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

Agughasi

2024

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Chronic Obstructive Pulmonary Disease (COPD) is a predominant global health concern, ranking third in mortality rates, yet frequently remains undiagnosed until its advanced stages. Given its prevalence, the need for innovative and widely accessible diagnostic tools has never been more paramount. While spirometry tests serve as conventional diagnostic benchmarks, their reach remains limited, especially in regions with constrained medical resources. The presented research harnesses deep learning algorithms to facilitate early-stage COPD detection, specifically targeting Chest X-rays (CXRs). The clinically annotated VinDR-CXR dataset provides the primary foundation for model training, complemented by incorporating the ChestX-ray14 dataset for initial model pre-training. Such a dual-dataset strategy augments model generalization and adaptability. Among several explored Convolutional Neural Network (CNN) architectures, the Xception model emerges as a frontrunner. Through transfer learning methodologies, this model produces a noteworthy recall rate of 98.2%, markedly surpassing the metrics of the ResNet50 model. Recognizing the imperative for transparency in AI applications in medical imaging, the research integrates essential explainability approaches viz: Gradient Class Activation Mapping (Grad-CAM) and SHapley Additive exPlanations (SHAP). These techniques elucidate the AI’s decision-making process, offering invaluable visual and analytical insights for fostering trust among medical professionals. In essence, this study not only underscores the potential of integrating AI with medical imaging for COPD detection but also accentuates the pivotal role of transparency in AI-driven medical interventions.

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Section: Adapted Technique Via Transfer Learningmentioning

confidence: 99%

Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

Agughasi

2024

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Advances in Thermal Imaging: A Convolutional Neural Network Approach for Improved Breast Cancer Diagnosis

Cited by 1 publication

References 34 publications

Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

Leveraging Transfer Learning for Efficient Diagnosis of COPD Using CXR Images and Explainable AI Techniques

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