Advancements in Lung Cancer Diagnosis: A Comprehensive Study on the Role of PCA, LDA, and t-SNE in Deep Learning Frameworks
ID:123
Submission ID:314 View Protection:ATTENDEE
Updated Time:2024-10-07 19:16:10
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Virtual Presentation
Abstract
In the ever-evolving domain of medical imaging, the integration of deep learning techniques holds the promise of transformative advancements. This research delved into the potential of employing data transfer within deep learning architectures for the automated detection of three distinct lung cancer types. Leveraging sophisticated methodologies like linear discriminant analysis (LDA), t-SNE, and PCA, the study aimed to enhance accuracy and efficiency in detecting malignancies from lung CT scan images. On rigorous evaluation, the models demonstrated compelling accuracy rates: salivary gland-type lung tumors at 90.5%, pleomorphic (spindle/giant cell) carcinoma at 88.2%, and primary pulmonary sarcomas at 91.3%. Additionally, ROC curve analysis further highlighted the robust discriminative capability of the models across varied decision thresholds. The promising results accentuate the potential of integrating data transfer techniques with deep learning in a clinical setting. This research not only exhibits a significant stride in lung cancer detection but also paves the path for further innovations in automated medical image analysis.
Keywords
Data transfer,deep learning,lung cancer detection,dimensionality reduction,ROC curve analysis
Submission Author
Vikas B
Koneru Lakshmaiah Education Foundation; Bowrampet
Satya Sukumar Makkapati
Acharya Nagarjuna University
Srinivasa Rao Bogireddy
Horizon Systems Inc
Balamurugan K.S.
Karpaga Vinayaga College of Engineering and Technology
M Deepa
Sri Shakthi Institute of Engineering and Technology
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