| Summary: | Cancer remains a significant global cause of mortality, presenting substantial challenges for both medical practitioners and researchers. Recent data from a 2023 American Cancer Society study anticipate 238,340 new cases of lung cancer, with 127,070 resulting fatalities—constituting approximately 20% of all cancer-related deaths. Early detection plays a crucial role in improving survival rates across various cancer types, and precise classification based on medical images aids physicians in selecting optimal therapies to reduce cancer mortality. While extensive work has been conducted in lung cancer detection using Convolutional Neural Networks (CNN), challenges persist due to the complex structures present in CT scans. In addition, CNN models encounter challenges related to their functionality, such as choosing an ideal architecture, figuring out appropriate model parameters, and fine-tuning weights and biases. This paper introduces an innovative approach to lung cancer classification, utilizing deep learning techniques within a 1D framework based on pretrained neural network architectures. First, a 1D architecture is designed, and then the solution vector of the model is computed, providing a foundational understanding of the architectural framework. The study integrates three distinct pretrained classifiers—AlexNet, VGG-16, and VGG-19—into the 1D framework and trains them on a curated set of features crucial for accurate lung lesion classification. The proposed system produces promising results, with both AlexNet and VGG-19 achieving an impressive accuracy of 92%, while VGG-16 outperforms them, reaching the highest accuracy at 94.67%. This research underscores the potential of leveraging pretrained neural networks within a 1D framework to enhance lung cancer diagnosis, contributing to more effective and timely interventions in the battle against this deadly disease. The findings confirm that the hybrid algorithm offers a reliable solution for the classification of lung cancer. ©2024 IEEE.
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