Lung cancer remains one of the leading causes of mortality worldwide, reaffirming the need for early detection to shore up its survival rates. This study presents a Streamlit-based application that exploits deep learning models for fully automated portable lung cancer detection. The system digests medical imaging data with advanced Convolutional Neural Networks (CNNs), ResNet, and InceptionV3, trained on lung cancer datasets available in the public domain. Image processing methods, namely normalization, augmentation, and segmentation, are utilized to boost model performance. The proposed application engraves real-time inference, displays and options to export diagnostic results in an interactive interface. The models are evaluated based on accuracy, sensitivity, specificity, and F1-score, aiming to provide them with guaranteed reliability. By merging AI-driven diagnostics with a user-friendly web platform, this solution improves access for healthcare professionals and researchers. Future work will comprise types of additional cancer detection, cloud-based scalability, and improved model precision using heterogeneous datasets concerning AI-powered medical diagnoses.
Convolutional Neural Networks, AI-driven diagnostics, AI-powered medical diagnoses
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