Lung Cancer Classification using Densenet Multi Model Optimization Techniques

Unchecked cell growth in lung tissue is indicative of lung cancer. Early identification of cancerous cells in the lungs is essential for functions including the body's carbon dioxide and oxygen elimination. The potential influence on patient diagnosis and treatment has led to a great deal of interest in the application of deep learning to identify lymph node invasion in CT scan and histology pictures. This approach consists of three stages: preprocessing, feature extraction, and classification. In order to identify lung cancer, we suggest using DenseNet's capability to continually propagate learnt characteristics backward via each layer. Pre-processing methods like contrast enhancement and filtering can be used to reduce undesired noise in the incoming image. Next, optimization techniques like Otsu thresholding are used to achieve the required picture segmentation. Among the suggested classifiers is the DenseNet classifier. Structure, hyperparameter optimization, etc. are used in DenseNet's multi-process optimization. We use performance matrices like accuracy, precision, recall, specificity, sensitivity, and F_Measure to execute the suggested technique in MATLAB.