Framework for Early Detection of Lung Malignancies using Deep Learning Techniques

Abstract

Lung cancer is a disease that has a major impact on public health. This study suggests folding networks (CNNS) and Densen's approaches to support lung cancer recognition and classification. In various areas of pattern recognition and medical imaging, CNN and Densenet have demonstrated their effectiveness. In this study, many medical lung images were created using x-rays from people with lung cancer. The results show that it can be maliciously divided using CNN and Densenet architectures with 99 parameter accuracy.8%. This study contributes to the creation of a deep learning-based system for detecting and classifying lung cancer. The results could be the basis for creating a more accurate and productive diagnostic system for lung cancer.Lung cancer remains one of the most deadly types of cancer in the world, making early and accurate diagnosis essential. Damayanti et al. (2023) proposed a hybrid deep learning structure in which folding networks (CNNs) combined with DerSenet to distinguish between malignant and benign lung nodes using radiation imaging. Their model reached an impressive 99.8° campaign. This presented the complementary intensities of CNN properties extraction and Densett in close relations recording subtle lung pathology from CT images. (202They reported over 99.92% on training accuracy, which maintained a robust verification accuracy of 95.6% with an AUC of 0.9967, improving strong generalization skills from Densenet. Complementary studies use the advantages of DENNET 121. The Radioactive Extended Diagnostics Pipeline integrated 93.6% of the specificity of the CT node classification task, 93.6% of hybrid architecture, 93.6% of CNN-CNN security errors, and 93.6% of Dasreng United without shortage. Adenocarcinoma, squamous cell carcinoma and normal tissue classes.